Senior Applied Scientist, Industrial Robotics Group

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
• 5+ years of building machine learning models or developing algorithms for business application experience
• Experience delivering products to volume production
• 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
• 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