Applied Scientist, One MHS - Software, Controls, Science

What you'll do

• Research and build optimization and decision-making solutions for material handling equipment control and scheduling.
• Formulate fulfillment and manufacturing operations as optimization problems, and develop objectives balancing operational priorities.
• Build simulation environments, validate policies, and integrate them into production systems with monitoring and staged rollouts.

What they ask for

• 2+ years of building machine learning models or developing algorithms for business application experience.
• PhD in Operations Research, Statistics, Applied Mathematics, Engineering, Computer Science or related field.
• Experience in optimization mathematics such as linear programming and nonlinear optimization; proficiency in Python scripting language.
• Demonstrated experience developing and applying optimization or reinforcement learning solutions to real-world control, scheduling, or operations problems; experience with popular deep learning frameworks and RL tooling.

Nice to have

• First-author publications at top-tier machine learning, operations research, or control venues.
• Experience building discrete-event simulators, applying optimization or RL to analogous settings, or deploying models to production at scale.

Pay

142,800.00 - 193,200.00 USD annually (base salary); 142,800.00 - 193,200.00 USD annually (base salary)

Tools

Python, PyTorch, d3rlpy, Ray/RLlib, Gymnasium, Stable-Baselines3, Isaac Gym/Omniverse

AI focus

Applies optimization, reinforcement learning, and sequential decision-making to real-time fulfillment-center control and scheduling.