Senior Product Manager - Task Management

Role overview

Senior Product Manager - Task Management at GreyOrange

The role: Own Task Management: the layer that decides what work goes to which agent, in what order, right now.

Responsibilities

• You will negotiate those contracts rather than throw work over a wall.

Preferred qualifications and skills

• Nice to have CS, robotics, industrial engineering or EE degree.

Employment details

• Comfort with India and US working hours, since customers and part of the product org sit in the US.
• Location: Gurugram - HQ

Additional role details

• Robots, automated systems and humans.
• Customers do not buy a robot.
• They buy throughput.
• Task Management is the decision engine behind it.
• Get it right and a site absorbs a peak-season order profile without anyone noticing.
• Get it wrong and you have idle agents on one side of the floor, a queue building on the other, and orders missing their cutoff.
• Navigation decides how an agent gets there.
• You decide where it should go and why that job beat the others.
• Those two areas sit next to each other and constrain each other, which makes this one of the more interesting problems in the company.
• What you will own The allocation logic as a product.
• Task decomposition, assignment, prioritisation, batching and rebalancing.
• Not just the rules, but the model of how work should flow through a site.
• Contention and fairness.
• Hundreds of agents, mixed capability, competing orders and hard SLA cutoffs.
• You define how the system decides who waits.
• The measurement system.
• Order cycle time, SLA attainment, agent utilisation and idle time, queue depth, starvation, priority inversion, tasks per agent hour.
• Define what good looks like and make it observable.
• Internal tooling.
• Decision replay so the team can answer "why did agent 47 get that job", order-profile simulation, and what-if analysis on a new customer's order mix before we commit to numbers.
• The interface with Navigation and Peripherals.
• Your assignments create their load.
• Trade-off calls across throughput, order latency, fairness, agent wear and compute cost.
• What we expect in the first six months A baseline on utilisation and SLA attainment that leadership and the field both trust, with numbers nobody argues about.
• A simulation setup where a proposed allocation change can be evaluated against a real order profile before it reaches a customer floor.
• One measurable improvement a customer feels, in cycle time, utilisation or peak capacity.
• A clear position on where allocation should go next, and what we stop doing to get there.
• What you need Must have 7+ years in technical product management, or engineering then product, with real time on systems that control physical machines.
• You have shipped software that assigns work to physical resources.
• Robots, vehicles, riders, machines or crews.
• Something with travel time, capacity limits and a cost to being wrong.
• Fleet-scale experience.
• Assigning work to ten things is a different problem from assigning work to four hundred that interfere with each other.
• Depth on scheduling, task allocation, queueing, constraint solving or matching.
• Not writing the solver, but knowing when a greedy heuristic will stop holding and what it will cost.
• Demonstrated ability to build a measurement system from nothing.
• You have defined metrics, instrumented a system, and changed a team's behaviour with the data.
• Production environment exposure.
• You have debugged a live floor or a live network with operators standing next to you.
• Platform and API thinking.
• Task Management is consumed by other systems and must hold a contract.
• Written communication that is short and decisive.
• Strong preference Warehouse automation, AMR/AGV fleets, intralogistics or manufacturing execution.
• Simulation and scenario testing as a product surface, not just a QA activity.
• Heterogeneous fleets where agents have different capabilities and you did not control the hardware.
• You have run an allocation change against a live customer and know what that felt like.
• Operations research, optimisation or combinatorics background.
• Prior engineering role.