Sr. Machine Learning Engineer, Cell Manufacturing

What to Expect

Tesla is re-thinking how batteries are made from the ground up. We are designing new factories, new equipment, new processes, and new software to rapidly scale battery cell manufacturing. The primary bottleneck to Tesla's future expansion and the transition to sustainable transport and energy storage is our ability to produce and procure batteries - that is why we are innovating in-house, with our collection of world‑class engineers, to redefine the industry.

Data, data analytics, and automation all play a critical part in this strategy.

As part of the Global Cell Manufacturing Analytics team, you will drive analytics of critical and complex questions around process integration and overall factory optimization. While the role is based at Gigafactory Texas, you will support all of Tesla's cell manufacturing factories across North America and Europe. You will develop data analytics workflows and impact the design of data architecture and software solutions that will be deployed globally.

In this role, you will also develop data‑driven models and analyses that will help ramp up and optimize our cell factories as fast as possible by maximizing yield, throughput and effectiveness.

Your hands‑on analytics work will enable various engineering, operations, and executive customers at Tesla to leverage the vast amounts of data generated from the cell manufacturing process all the way to the global Tesla vehicle fleet, to design and produce safer, lower‑cost, and higher‑performance cells.

The work environment is intellectually demanding, fast paced, and incredibly exciting. You should be ready to push your limits, as you join a highly motivated and capable global team to achieve incredible goals.

• Design, build, and optimize ML models for Tesla cell manufacturing challenges such as predictive maintenance, defect detection via vision systems, and process improvements to boost yield and efficiency
• Apply learning techniques (supervised, unsupervised, reinforcement) with exploratory data analysis and feature engineering to extract actionable insights from datasets like images, time‑series, and sensor data
• Build and maintain scalable data pipelines for real‑time processing of high-volume production data, supporting ML inference and analytics needs
• Develop validation, testing, and monitoring frameworks to ensure model reliability and enable retraining in dynamic factory environments
• Deploy models into production with containerization and API integration while creating visualizations/dashboards and handling data requests for stakeholders
• Collaborate with cross‑functional teams in engineering, quality, and materials science to define problems, refine solutions, and integrate ML tools for measurable, sustainable impact

• Degree in quantitative discipline (e.g., Machine learning, Statistics, Computer Science, Mathematics, Mechanical/Electrical/Chemical/Industrial Engineering) or equivalent experience
• 4+ years industrial experience preferred (manufacturing, semiconductor, automobiles, pharmaceuticals, engineering, etc.)
• Hands‑on experience building and deploying ML models using frameworks like PyTorch, Tensor Flow, scikit‑learn, Num Py, XGBoost, and OpenCV
• Knowledge of the full ML lifecycle, including development, tuning, validation, deployment, monitoring, and analytics integration
• Expertise in data pipelines and orchestration (e.g., Airflow, Kubeflow), with strong skills in Python, SQL, and distributed systems (e.g., Spark, Click House) for industrial data handling
• Experience with Git or other source control software
• Ability to analyze diverse data types (e.g., images, time‑series) and apply big data tools for real‑time processing and insight generation
• Proficiency in statistical analysis and visualization (e.g., Grafana) to communicate findings clearly to technical and non‑technical audiences
• Strong communication and collaboration skills, with adaptability to fast‑paced, cross‑functional environments
• Nice to have:
  Experience with edge computing, Docker, or manufacturing process development or controls in battery/EV production

Along with competitive…