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Applied Scientist- Physical AI for Manufacturing, OMHS SCS

Applied Scientist- Physical AI for Manufacturing, OMHS SCS at Amazon scores 96 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

US, MA, Bostonfull-time$143k-$193k

AI in this role

computer-vision

As an Applied Scientist on the OMHS Software Controls and Science team, you will build and deploy first-of-its-kind physical AI capabilities within Amazon Manufacturing Services. You will turn applied research into functional prototypes and production systems that operate on real parts under real shop floor conditions. This role combines applied ML, 3D perception, and geometric reasoning with a strong manufacturing-outcome focus. It will be your job to frame ambiguous fabrication problems as tractable scientific problems, and to deploy perception and part-recognition systems on robotic welding cells that
match physical parts to their CAD models an determine orientation, tooling, and fixturing without manual programming. You will own the full loop from prototype to measurable result: instrumenting robotic cells with sensors, collecting real-world manufacturing data, training and iterating models against accuracy and cycle-time targets, and integrating your work into robot motion planning and production software pipelines. Your near-term mission is to help enable a lossless digital thread from engineering specification to autonomous robotic execution, starting with welding, in a high-mix environment scaling toward high-volume production.

Key job responsibilities
• Deploy 3D perception and part-recognition systems on robotic welding cells that match physical parts to their CAD models and determine part orientation, tooling, and fixturing without manual programming.

• Build software pipelines that connect CAD and engineering-drawing interpretation to robot motion planning and execution in a production manufacturing environment.

• Frame ambiguous fabrication problems as tractable scientific problems, and prototype solutions end to end — from sensor setup and data collection to model training, deployment, and performance evaluation.

• Evaluate, recommend, and integrate commercial robotic platforms, 3D scanning systems, and sensor hardware to support physical AI development on the shop floor.

• Instrument robotic cells, collect real-world manufacturing data, and iterate on model performance against measurable accuracy and cycle-time targets.

• Collaborate across automation engineering and software engineering teams to integrate your solutions into the shop floor deployment architecture.

About the team
Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised.

The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware
prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments,
develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.

Basic qualifications

- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- 1+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience
- Hands-on experience deploying robots or autonomous systems in real-world environments.
- Ability to debug the full stack from perception to control.

Preferred qualifications

- Experience with robotic manipulation platforms.
- Experience with realtime sensor fusion (e.g. LiDAR, camera, radar).
- Publications in your field (CVPR, ICCV, RSS, ICRA preferred).

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, MA, Boston - 142,800.00 - 193,200.00 USD annually
USA, MA, N.Reading - 142,800.00 - 193,200.00 USD annually

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Computer Vision

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer Vision. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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