Description

Amazon Industrial Robotics is seeking exceptional applied science talent to develop AI and machine learning systems that will enable the next generation of advanced manufacturing capabilities at unprecedented scale. We're building revolutionary software infrastructure that combines cutting-edge AI, large-scale optimization, and advanced manufacturing processes to create adaptive production control systems.

As a Senior Applied Scientist, you will develop and improve machine learning systems that enable real-time manufacturing flow decisions. You will leverage state-of-the-art optimization and ML techniques, evaluate them against representative manufacturing scenarios, and adapt them to meet the robustness, reliability, and performance needs of production environments. You will invent new algorithms where gaps exist. You'll collaborate closely with software engineering, manufacturing engineering, robotics simulation, and operations teams, and your outputs will directly power the systems that determine what to build next, where to allocate resources, and how to maximize throughput.

The ideal candidate brings deep expertise in optimization and machine learning, with a proven track record of delivering scientifically complex solutions into production. You are hands-on, writing significant portions of critical-path scientific code while driving your team's scientific agenda. If you're passionate about inventing the intelligent manufacturing systems of tomorrow rather than optimizing those of today, this role offers the chance to make a lasting impact on the future of automation.

Key job responsibilities
- Identify and devise new scientific approaches for constraint identification, dispatch optimization, WIP release control, and predictive flow intelligence when the problem is ill-defined and new methodologies need to be invented
- Lead the design, implementation, and successful delivery of scientifically complex solutions for real-time manufacturing flow optimization in production
- Design and build ML models and optimization algorithms including constraint prediction, starvation risk forecasting, and dispatch optimization
- Write a significant portion of critical-path scientific code with solutions that are inventive, maintainable, scalable, and extensible
- Execute rapid, rigorous experimentation with reproducible results, closing the gap between simulation and real manufacturing environments
- Build evaluation benchmarks that measure model performance against manufacturing outcomes including constraint utilization and throughput rather than traditional ML metrics alone
- Influence your team's science and business strategy through insightful contributions to roadmaps, goals, and priorities
- Partner with manufacturing engineering, robotics simulation, and applied intelligence teams to ensure scientific approaches are grounded in operational reality
- Drive your team's scientific agenda and role model publishing of research results at peer-reviewed venues when appropriate and not precluded by business considerations
- Actively participate in hiring and mentor other scientists, improving their skills and ability to deliver
- Write clear narratives and documentation describing scientific solutions and design choices

Basic Qualifications

- Knowledge of programming languages such as C/C++, Python, Java or Perl
- PhD in computer science, operations research, machine learning, industrial engineering, or a related quantitative field, or Master's degree plus 4+ years building ML models and algorithms in applied settings
- 5+ years of experience applying machine learning, optimization, or decision systems to complex real-world problems
- Proven track record of delivering scientifically complex solutions into production
- Deep expertise in one or more of: combinatorial optimization, reinforcement learning, constraint programming, or stochastic modeling
- Ability to design rigorous experiments, analyze results, and iterate quickly with reproducible baselines
- Demonstrated technical contributions through publications, patents, or impactful production systems

Preferred Qualifications

- 8+ years of experience in applied science or research with progressive scope and impact
- Experience with manufacturing systems, production scheduling, supply chain optimization, or industrial process control
- Experience with Theory of Constraints or flow-based production optimization
- Experience with real-time decision systems that operate under uncertainty
- Experience with sim-to-real transfer or physics-informed machine learning
- Experience with AWS services including SageMaker, and familiarity with MLOps practices
- Experience with AI-native development practices and AI coding assistants
- Knowledge of manufacturing execution systems (MES), SCADA, ERP, or related industrial software

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.