Staff Research Scientist

Job Description

Our Opportunity: Chewy Robotics is seeking a Staff Research Scientist to advance the orchestration of heterogeneous fleets of humanoid and mobile robots by tackling some of the hardest systems problems in robotics and Physical AI, based out of our state-of-the-art Robotics Lab in Woburn, MA. You will work at the intersection of multi-agent systems, optimization, semantic mapping, federated learning, and learning-based planning, designing and deploying algorithms that decide which robot does what, when, and where.

In this role, you will own and drive portions of the fleet intelligence roadmap, leading research programs end-to-end from problem framing through publication-quality evaluation and lab validation. You will lead the development of multi-agent control and sensing systems that enable mobile robotic systems to perform orchestrated tasks for manipulation and monitoring applications. Areas of interest include but are not limited to SLAM, federated learning, perception systems, task allocation, multi-agent path finding, and multi-agent control architectures.

An ideal candidate possesses broad technical mastery with demonstrated technical leadership in at least one relevant research area, as well as robotics system design, algorithm development, experimental validation, excellent cross-functional collaboration abilities, and routinely using AI coding and writing tools to accelerate algorithm development, experimentation, and business communication. You will validate ideas in simulation and experiments, and the expectation is that your work results in publishable research at top-tier venues, reusable components, and real-world deployable methods that shape Physical AI at Chewy and inform the broader robotics community.

What You’ll Do

• Lead research and technical strategy for one or more core fleet-intelligence areas, such as:
• Multi-agent perception and estimation
• Large-scale multi-robot task allocation and scheduling (LSTA)
• Multi-agent path finding (MAPF), routing, and congestion-aware coordination
• Fleet-level safety supervision and constraint-aware decision-making
• Learning-assisted planning and decision-making (RL/IL/model-based approaches)
• Research and design control methods for multi-agent robotic systems, such as collaborative and active SLAM, multi-robot task allocation, routing, estimation, and fleet-level decision-making under real-world constraints (battery, congestion, different sensing modalities, safety zones, SLAs)
• Design and run simulation-first experiments, define metrics for throughput, coordination latency/replanning responsiveness, robustness, and safety-event rate, comparing algorithms against strong baselines, instrumenting experiments, and analyzing fleet telemetry to understand performance and failure modes at scale
• Collaborate with scientists and engineers to develop pipelines for deploying the methods to real-world heterogeneous robotic systems
• Collaborate with scientists and engineers to integrate learning-based control into orchestration, working with RL/IL/model-based controllers for navigation, manipulation, or local behaviors and defining interfaces, rewards, and telemetry for end-to-end learning loops
• Build benchmarks and research artifacts, including reusable datasets, experiment harnesses, and publications for top-tier venues, while mentoring co-ops and junior contributors on related projects
• Contribute to the architecture and deployment of distributed robotic systems, including communication frameworks and APIs for full-scale deployment
• Utilize modern simulation and training environments, such as Isaac Lab or Gym for robot learning, and Isaac Sim, Gazebo, or Mujoco for robotic system modeling and validation
  

What You’ll Need

• PhD from an internationally recognized institution in Robotics, Computer Science, Electrical/Mechanical Engineering, Applied Mathematics, Operations Research, or a related field, with focus on controls, optimization, multi-agent systems, or AI for physical systems
• Deep expertise in at least one of the following, and demonstrated experience with several of:
• Estimation and mapping for multi-agent systems (e.g., C-SLAM, semantic 3D mapping, active SLAM, multi-modal sensor fusion)
• Multi-agent systems, task allocation, or fleet-level scheduling
• Motion planning or multi-agent path finding (MAPF, routing, graph search, or sampling-based planning)
• Control theory and state estimation for robotic systems (e.g., MPC, impedance control, Kalman filtering, particle filtering)
• Machine learning for decision-making (e.g., reinforcement learning, imitation learning, model-based planning)
• Familiarity with robot perception (vision, tactile, or multimodal sensing) for navigation, manipulation, or safety monitoring
• Experience with robotics middleware and simulation (e.g., ROS/ROS2 and simulators such as MuJoCo, Isaac Sim, PyBullet, or Gazebo)
• Proven track record of peer-reviewed publications and innovative research, including evidence of leading projects that deliver measurable outcomes
• Strong programming skills in at least one of Python, C/C , ROS/ROS2, Julia
• Excellent written and verbal communication skills for both technical and cross-functional audiences; ability to drive alignment on goals, metrics, and tradeoffs
  

Preferred Experience

• Demonstrated experience working with heterogeneous robot fleets and capability-aware sensing, scheduling, or routing
• Experience designing or contributing to orchestration systems: fleet management, dispatch, or industrial automation at scale
• Background in learning-based control (RL, IL, sim-to-real) for robotic or simulated agents
• Exposure to safety-critical or high-availability systems, including safety envelopes and fallback behaviors
• Experience with tools such as PyTorch or JAX, and/or modern optimization libraries/solvers (e.g., OR-Tools, Gurobi, CasADi)
• Experience mentoring students, co-ops, or interns and collaborating with academic partners
• Demonstrated experience in real-time/embedded systems
• Experience using AI coding and writing tools to accelerate algorithm development, experimentation, and business communication
  
  

The base salary range for this role is $186,500 - $265,000.00.

• The specific salary offered to a candidate may be influenced by a variety of factors including but not limited to the candidate’s relevant experience, education, and work location. In addition, this position is eligible for 401k and a new hire and annual equity grant. C08 positions may also be eligible for annual bonus.
  
  

We offer different types of insurance and benefits, such as medical/Rx, vision, dental, life, disability, hospital indemnity, critical illness, and accident. We offer parental leave, family services benefits, backup dependent care, flexible spending accounts, telemedicine, pet adoption reimbursement, employee assistance program, and many discounts including 10% off pet insurance and 20% off at Chewy.com.

Exempt salary team members have unlimited PTO, subject to manager approval. Team members will receive six paid holidays per year. Team members may be eligible for paid sick and family leave in compliance with applicable state and local regulations.

Chewy is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, gender, citizenship, marital status, religion, age, disability, gender identity, results of genetic testing, veteran status, as well as any other legally-protected characteristic. If you have a disability under the Americans with Disabilities Act or similar law, and you need an accommodation during the application process or to perform these job requirements, or if you need a religious accommodation, please contact CAAR@chewy.com.

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