Lead Performance and Optimization Engineer

AMD · Bangalore

Source and collection record

Last observed by our collector: 9 October 2026 at 12:45 am IST. This is not a manual review or a confirmation from the employer.

Employer requisition: 80091

Job type and experience labels are automated discovery categories. The employer determines eligibility. Original experience wording was not retained separately; check the current employer page.

How sourcing and classification work · Report a discrepancy

Your application evidence checklist

Mark the statements you can support after reading the employer source. This checklist stays on this page; it does not score your eligibility or submit an application.

Collected minimum qualification: Benefits offered are described:  <a href="https://www.amd.com/en/corporate/careers/benefits.html" target="_blank" rel="noopener">AMD benefits at a glance</a>. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position.  AMD’s “Responsible AI Policy” is available <a href="https://www.amd.com/en/legal/responsible-ai-use-policy.html" target="_blank" rel="noopener">here.</a> This posting is for an existing vacancy.Attendance and availability: I checked the employer page for work location, office attendance, start date and duration.Current requisition: I checked the current employer requisition and its application requirements.

0 of 3 checks marked. Keep unresolved requirements as questions for the employer.

Collected role description

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. Lead Performance and Optimization Engineer THE ROLE: We are seeking a Performance Engineer with strong expertise in serverclass CPUs, CPU microarchitecture, and ML inference, responsible for benchmarking, analysing, and optimizing CPU inference performance using EPYCoptimized ML libraries (e.g., ZenDNN) with common frameworks (PyTorch, TensorFlow, ONNX Runtime). The role includes handson work in performance debugging, OS/BIOS tuning, thread/core affinity, multiinstance execution, and Python/scriptingbased automation. .<span style="margin: 0px; padding: 0px;" data-ccp-charstyle="eop" data-ccp-charstyle-defn="{"ObjectId":"065fb625-426b-4ae2-8ee9-a7c4873db88c|130","ClassId":1073872969,"Properties":[469775450,"eop",201340122,"1",134233614,"true",469778129,"eop",335572020,"1",469778324,"Default Paragraph Font"]}"> THE PERSON: The ideal candidate should be passionate about software engineering and possess leadership skills to drive sophisticated issues to resolution. Able to communicate effectively and work optimally with different teams across AMD. KEY RESPONSIBILITIES: <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1">Performance Engineering & Optimization <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Run and optimize ML inference workloads on CPUs using EPYCoptimized libraries (ZenDNN), improving throughput/latency across singleinstance and multiinstance scenarios. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Configure and tune NUMA, HugePages, SMT, power/performance modes, CPU isolation, scheduler settings, scaling governors, and other OS/BIOS parameters. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Design and validate thread/core affinity strategies for singleinstance, multiinstance, multisocket, and frameworklevel multiinstance execution models. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="4" data-aria-level="1">Optimize workload behaviour through NUMAaware locality, thread scheduling/pinning, batch size tuning, operatorlevel parallelism, and other CPUfocused techniques. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="5" data-aria-level="1">Contribute to multiinstance execution framework development, including policies for instance partitioning, core allocation, memory distribution, and orchestration of parallel runs on large EPYC systems. </li></ul>Benchmarking & Analysis <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Develop and run structured benchmarks across EPYC SKUs, core counts, caching/topology variations, sockets, and diverse batch sizes. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Analyze scaling for singleinstance vs. multiinstance execution, instance placement strategies, and workload isolation. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Use perf, VTune, ftrace/tracecmd, PMU counters, flame graphs to identify bottlenecks in compute, memory, thread scheduling, or instancelevel competition. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="4" data-aria-level="1">Perform rootcause analysis for regressions in latency, throughput, multiinstance efficiency, memory bandwidth, and pipeline behaviour. </li></ul>ML Inference Domain Knowledge <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="10" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Understand how ML frameworks execute models on CPU, including tensor shapes/layouts, operator behavior, threading models, kernel dispatch, scheduling strategies, and multiinstance runtime interactions. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="10" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Interpret how model architecture and operator composition influence performance across single and multiple concurrent inference instances. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="10" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Collaborate with ZenDNN and kernel/ops teams to relay findings and help guide kernel/operatorlevel improvements. </li></ul>Automation & Tooling <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Build automation pipelines for singleinstance and multiinstance benchmarking, profiling, orchestration, scaling studies, and regression detection. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Develop Python/Bash tooling that manages instance spawning, CPU core partitioning, memory pinning, performance data capture, reporting, and visual dashboards. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Maintain reproducible experiment workflows for both singleinstance and multiinstance configurations. </li></ul> Required Skills & Qualifications <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Strong understanding of CPU architecture: pipelines, caches, TLB, NUMA, SMT/HT, vector units (AVX2/AVX512/VNNI/BF16/INT8), and memory hierarchy. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">8 to 12 years in performance engineering, systems optimization, or lowlevel execution on Linux. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Handson experience with Linux tuning and serverclass OS/BIOS configuration. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="4" data-aria-level="1">Proficiency with perf, VTune, PMU counters, ftrace/tracecmd, flame graphs, and multiinstance profiling. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="5" data-aria-level="1">Strong knowledge of ML inference execution (tensors, operators, threading models) on CPU backends. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="6" data-aria-level="1">Strong Python and Bash for automation and performance tooling. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="7" data-aria-level="1">Experience in multicore scaling, thread affinity, scheduler behavior, concurrency techniques, and multiinstance execution strategies. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="8" data-aria-level="1">Familiarity with PyTorch, TensorFlow, ONNX Runtime for running inference workloads. </li></ul> </li></ul>#LI-SG2 Benefits offered are described: <a href="https://www.amd.com/en/corporate/careers/benefits.html" target="_blank" rel="noopener">AMD benefits at a glance</a>. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available <a href="https://www.amd.com/en/legal/responsible-ai-use-policy.html" target="_blank" rel="noopener">here.</a> This posting is for an existing vacancy.

Collected minimum qualification

Benefits offered are described:  <a href="https://www.amd.com/en/corporate/careers/benefits.html" target="_blank" rel="noopener">AMD benefits at a glance</a>. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position.  AMD’s “Responsible AI Policy” is available <a href="https://www.amd.com/en/legal/responsible-ai-use-policy.html" target="_blank" rel="noopener">here.</a> This posting is for an existing vacancy.

Collected preferred qualification

Lead Performance and Optimization Engineer  THE ROLE: We are seeking a Performance Engineer with strong expertise in serverclass CPUs, CPU microarchitecture, and ML inference, responsible for benchmarking, analysing, and optimizing CPU inference performance using EPYCoptimized ML libraries (e.g., ZenDNN) with common frameworks (PyTorch, TensorFlow, ONNX Runtime). The role includes handson work in performance debugging, OS/BIOS tuning, thread/core affinity, multiinstance execution, and Python/scriptingbased automation.  .<span style="margin: 0px; padding: 0px;" data-ccp-charstyle="eop" data-ccp-charstyle-defn="{"ObjectId":"065fb625-426b-4ae2-8ee9-a7c4873db88c|130","ClassId":1073872969,"Properties":[469775450,"eop",201340122,"1",134233614,"true",469778129,"eop",335572020,"1",469778324,"Default Paragraph Font"]}">   THE PERSON: The ideal candidate should be passionate about software engineering and possess leadership skills to drive sophisticated issues to resolution. Able to communicate effectively and work optimally with different teams across AMD.   KEY RESPONSIBILITIES: <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1">Performance Engineering & Optimization <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Run and optimize ML inference workloads on CPUs using EPYCoptimized libraries (ZenDNN), improving throughput/latency across singleinstance and multiinstance scenarios. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Configure and tune NUMA, HugePages, SMT, power/performance modes, CPU isolation, scheduler settings, scaling governors, and other OS/BIOS parameters. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Design and validate thread/core affinity strategies for singleinstance, multiinstance, multisocket, and frameworklevel multiinstance execution models. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="4" data-aria-level="1">Optimize workload behaviour through NUMAaware locality, thread scheduling/pinning, batch size tuning, operatorlevel parallelism, and other CPUfocused techniques. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="5" data-aria-level="1">Contribute to multiinstance execution framework development, including policies for instance partitioning, core allocation, memory distribution, and orchestration of parallel runs on large EPYC systems. </li></ul>Benchmarking & Analysis <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Develop and run structured benchmarks across EPYC SKUs, core counts, caching/topology variations, sockets, and diverse batch sizes. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Analyze scaling for singleinstance vs. multiinstance execution, instance placement strategies, and workload isolation. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Use perf, VTune, ftrace/tracecmd, PMU counters, flame graphs to identify bottlenecks in compute, memory, thread scheduling, or instancelevel competition. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="4" data-aria-level="1">Perform rootcause analysis for regressions in latency, throughput, multiinstance efficiency, memory bandwidth, and pipeline behaviour. </li></ul>ML Inference Domain Knowledge <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="10" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Understand how ML frameworks execute models on CPU, including tensor shapes/layouts, operator behavior, threading models, kernel dispatch, scheduling strategies, and multiinstance runtime interactions. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="10" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Interpret how model architecture and operator composition influence performance across single and multiple concurrent inference instances. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="10" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Collaborate with ZenDNN and kernel/ops teams to relay findings and help guide kernel/operatorlevel improvements. </li></ul>Automation & Tooling <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Build automation pipelines for singleinstance and multiinstance benchmarking, profiling, orchestration, scaling studies, and regression detection. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Develop Python/Bash tooling that manages instance spawning, CPU core partitioning, memory pinning, performance data capture, reporting, and visual dashboards. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 48px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Maintain reproducible experiment workflows for both singleinstance and multiinstance configurations. </li></ul> Required Skills & Qualifications <ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Strong understanding of CPU architecture: pipelines, caches, TLB, NUMA, SMT/HT, vector units (AVX2/AVX512/VNNI/BF16/INT8), and memory hierarchy. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">8 to 12 years in performance engineering, systems optimization, or lowlevel execution on Linux. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Handson experience with Linux tuning and serverclass OS/BIOS configuration. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="4" data-aria-level="1">Proficiency with perf, VTune, PMU counters, ftrace/tracecmd, flame graphs, and multiinstance profiling. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="5" data-aria-level="1">Strong knowledge of ML inference execution (tensors, operators, threading models) on CPU backends. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="6" data-aria-level="1">Strong Python and Bash for automation and performance tooling. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="7" data-aria-level="1">Experience in multicore scaling, thread affinity, scheduler behavior, concurrency techniques, and multiinstance execution strategies. </li></ul><ul style="margin: 0px; padding: 0px; font-family: verdana;"><li style="margin: 0px 0px 0px 24px; padding: 0px; font-size: 12pt; font-family: arial, helvetica, sans-serif;" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="8" data-aria-level="1">Familiarity with PyTorch, TensorFlow, ONNX Runtime for running inference workloads. </li></ul> </li></ul>#LI-SG2

Collected experience requirement

12 years