Research Engineer

• Design, implement, and train causal graph models and planning algorithms
• Architect approaches to multi-objective optimization
• Build and maintain training and inference pipelines
• Manage experiment tracking, model versioning, dataset loaders, and objectives and interfaces
• Package trained models as containerized inference components conforming to specified APIs
• Work with the Lead System Integrator on regular code drops and major code revisions
• Contribute to synthetic data generation and simulation campaigns
• Present research results at program design reviews, site visits, and PI workshops
• Author technical sections of monthly status reports and design documentation
• Coordinate with academic subcontractor researchers on shared model components

AI/ML Research Engineer

• Serve as the Innovation Lab's lead software engineer and technical authority
• Translate business problems and emerging opportunities into prototypes, proofs of concept, pilots, and production-ready solutions
• Lead technical delivery of multiple innovation initiatives across the enterprise
• Accelerate experimentation and reduce time from idea to proof of value
• Help transition successful solutions into broader delivery teams
• Provide technical leadership and architecture direction
• Design, build, and review full-stack software solutions and AI-enabled applications
• Rapidly prototype concepts and validate ideas with business stakeholders
• Establish engineering standards, development practices, and technical governance
• Partner with ED&T, cybersecurity, architecture, data, and business teams
• Mentor engineers and build the lab's long-term technical capabilities
• Evaluate emerging technologies for applicability to Stryker

Senior Principal Engineer, Innovation Lab

• Lead the design and implementation of the machine learning core for research and development into generative models
• Formulate models and build training and inference pipelines
• Integrate models with synthetic and laboratory datasets
• Deliver trained, containerized frameworks
• Design, implement, and train causal graph models and planning algorithms
• Architect approaches to multi-objective optimization
• Build and maintain ML infrastructure, including training and inference pipelines, experiment tracking, model versioning, dataset loaders, and objectives and interfaces
• Package trained models as containerized inference components conforming to specified APIs
• Work with the Lead System Integrator to deliver regular code drops and major code revisions
• Contribute to synthetic data generation and simulation campaigns
• Present research results at program design reviews, site visits, and PI workshops
• Author technical sections of monthly status reports and design documentation
• Coordinate with academic subcontractor researchers on shared model components
• Collaborate with RF, DSP, and formal methods engineers