Sr. Machine Learning Engineer

At Tesla, this role supports the Tesla Construction organization by building backend machine learning capabilities that can move from experimentation to dependable production systems. You will own a technical path that spans problem definition, data pipelines, model training and serving, and operational monitoring for model-driven workflows.

This position is based in Fremont, CA (onsite) and focuses on delivering forecasting, classification, anomaly detection, and decision-support through scalable ML services and APIs.

What you’ll do

• Design and build backend ML services and APIs that enable forecasting, classification, anomaly detection, and decision-support use cases
• Own the end-to-end path from data to production, including feature pipelines, model training, evaluation, deployment, and monitoring
• Work with product and business stakeholders to define problems, success metrics, and how model outputs fit into existing workflows
• Integrate models into scalable backend systems with clear contracts, versioning, and failure handling
• Enhance reliability, latency, and data quality in production using logging, drift monitoring, and retraining strategies
• Review data, features, and model design with the team and provide constructive technical feedback
• Mentor engineers on ML engineering practices, testing, and production standards
• Share knowledge across software, data, and operations teams, communicating trade-offs in plain language

What you bring

• Bachelor’s Degree in Computer Science, Machine Learning, or a related field, or equivalent experience
• 5+ years building production backend systems, including 3+ years shipping machine learning models or ML services
• Strong proficiency in Python for data processing, model development, and service development
• Experience with ML frameworks such as scikit-learn, PyTorch, or similar, including model evaluation and experiment tracking
• Experience designing and serving models behind APIs, including REST service design, versioning, and integration patterns
• Strong SQL skills and experience with relational databases such as SQL Server, including feature and data pipeline design
• Experience productionizing models with Docker, CI/CD, and cloud platforms (Azure preferred)
• Working knowledge of performance, reliability, logging, monitoring, and scalability for production ML systems
• Experience writing unit and integration tests for data pipelines, model logic, and services
• Experience mentoring engineers and driving technical decisions from problem framing through deployment

Technologies

• Python, scikit-learn, PyTorch
• REST, SQL, SQL Server
• Docker, CI/CD, Azure
• Unit tests, integration tests
• Feature pipelines, model training, model evaluation, experiment tracking
• Logging, drift monitoring

Compensation and benefits

Salary: USD 124,000 - 258,000 per year, plus cash and stock awards and benefits.

• Medical plans with options including $0 payroll deduction
• Family-building, fertility, adoption and surrogacy benefits
• Dental (including orthodontic coverage) and vision plans, with options including $0 paycheck contribution
• Company paid HSA contribution when enrolled in the High-Deductible medical plan with HSA
• Healthcare and Dependent Care Flexible Spending Accounts (FSA)
• 401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits
• Company paid Basic Life and AD&D
• Short-term and long-term disability insurance (90 day waiting period)
• Employee Assistance Program
• Sick and Vacation time (Flex time for salary positions, Accrued hours for Hourly positions), and Paid Holidays
• Back-up childcare and parenting support resources
• Voluntary benefits including critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
• Weight Loss and Tobacco Cessation Programs
• Tesla Babies program, commuter benefits, and employee discounts and perks
• Earned Wage Access: real-time pay access paid daily, subject to eligibility and schedule (applicable for below Sr. Managers levels)

Education

Bachelor’s Degree in Computer Science, Machine Learning, or a related field, or equivalent experience.