Senior Applied Scientist, Amazon Industrial Robotics

Senior Applied Scientist, Amazon Industrial Robotics

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Job ID: 10378426 | Services LLC

Amazon Industrial Robotics is seeking exceptional applied science talent to develop AI and machine learning systems that will enable continuous learning, fleet-wide intelligence, and performance optimization for advanced robotics operations at unprecedented scale. We're building revolutionary software infrastructure that combines AI, large-scale data systems, and continuous learning pipelines to create intelligent systems that enable robots to improve continuously from real-world experience.

As an Applied Scientist III, you will develop and improve machine learning systems that enable robots to learn from deployed fleet experience and continuously improve performance. You will leverage state-of-the-art ML techniques, evaluate them against representative robotics tasks and operational 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 robotics teams, software engineering, manufacturing optimization, and operations teams, and your outputs will directly power the systems that enable robots to get smarter over time.

The ideal candidate brings deep expertise in machine learning and large-scale data systems, 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 building the intelligent systems that enable robots to learn and improve from every task they perform, 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 continuous learning, fleet optimization, predictive analytics, and performance 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 continuous learning pipelines, fleet optimization, and predictive maintenance in production
• Design and build ML models including reinforcement learning training infrastructure, anomaly detection systems, predictive maintenance models, and fleet optimization algorithms
• 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-world robotics environments
• Build evaluation benchmarks that measure model performance against operational outcomes including fleet reliability, prediction accuracy, and learning velocity rather than traditional ML metrics alone
• Influence your team's science and business strategy through insightful contributions to roadmaps, goals, and priorities
• Partner with robotics teams, manufacturing optimization, and fleet systems 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

• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
• PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 12+ years of building machine learning models or developing algorithms for business application experience
• 5+ years of practical work applying ML to solve complex problems experience
• Experience in several of the following areas: machine learning, statistics, deep learning, natural language processing, or information retrieval
• Demonstrated technical contributions through publications, patents, or impactful production systems

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.
• Experience building large-scale machine learning and AI solutions at Internet scale
• Experience with data infrastructures: relational analytic DBMS, Elastic-Search, and Big Data