Sr. Data Algorithm Engineer

What to Expect

We are looking for a highly skilled and motivated Sr. Data Algorithm Engineer to help scale Tesla's Service Supply Chain Platform and develop novel optimization techniques for a rapidly growing global service network. You will join the Supply Chain Optimization team, which drives strategic programs and continuous improvement across Tesla's Global Service Distribution Network. As Tesla's fleet expands, so does the scale and complexity of the network that keeps it serviced — more parts, more nodes, tighter delivery expectations. Your work will directly shape how that network is designed, planned, and run.

This role lives at the crossroads of supply chain, software engineering, and operations research. You should be equally at home writing production-grade code and applying optimization, ML, and simulation techniques to messy, real-world operational problems. We are looking for someone who does not stop at a notebook prototype but takes a model all the way into a reliable, maintained system that the business runs on every day.

You will build strong cross-functional partnerships with Material Planning, Logistics, Finance, Warehouse Operations, and Service Operations, and develop deep subject matter expertise in our systems, sourcing, planning, and fulfillment processes. Success requires a sharp business focus, a collaborative working style, and a proactive, critical mindset — the ability to think strategically while staying comfortable in the details that drive operational results. You will manage multiple concurrent projects of department-level scale and global reach.

What You’ll Do

• Expand our Digital Twin capabilities for end-to-end service supply chain visibility — enabling what-if simulation and network design to keep pace with the growing complexity and scale of Tesla's service business
• Apply modern machine learning, reinforcement learning, and mathematical optimization to high-impact supply chain problems spanning warehousing, slotting, consolidation, logistics, and last-mile fulfillment
• Architect, own, and operate end-to-end production ML pipelines — including CI/CD, model versioning, and orchestration — delivering scalable, modular, reliable, and high-performance systems across multiple supply chain domains
• Develop AI tools and agentic capabilities that answer day-to-day operational questions, and contribute to the multi-agent orchestration framework powering what-if simulation and decision support
• Stress-test current and future network designs through simulation and sensitivity analysis, surfacing bottlenecks, capacity constraints, and operational risks that inform strategic decisions
• Translate complex quantitative results into clear narratives and visualizations, enabling leadership to make informed tradeoffs between inventory investment, service levels, and operational complexity across the global supply chain
• Build and own decision support systems, tools, and models — driving implementation end to end with a bias toward speed, accuracy, cost efficiency, capacity, and flexibility

What You’ll Bring

• Degree in Computer Science, Computer Engineering, AI/ML, Applied Mathematics, Operations Research, Industrial Engineering, or a related field with 2+ years of hands-on industry experience building and deploying ML and optimization models in production, or equivalent experience
• Strong software engineering fundamentals — clean, testable, maintainable code and solid data engineering practices (pipelines, data modeling, performance at scale)
• Strong mathematical and algorithmic fundamentals, particularly in optimization, stochastic modeling, simulation, or reinforcement learning
• Proficiency in Python and SQL; experience with ML frameworks such as Py Torch or Tensor Flow, and optimization/simulation tooling (e.g., Gurobi, CPLEX, OR-Tools, Any Logic, Sim Py)
• Experience productionizing ML systems: CI/CD, model versioning, orchestration (e.g., Airflow, Kubeflow), containerization, and cloud or distributed compute
• Exposure to LLMs, agentic frameworks, and Generative AI applied to practical operational problems is a strong plus; excellent programming, debugging, performance analysis, and familiarity with modern development tooling (Git, JIRA)
• Demonstrated ability to work independently, lead initiatives end to end, and communicate insights and progress clearly to technical and executive stakeholders