Reinforcement Learning Engineer, Policy, Digital Optimus

What to Expect

As a Reinforcement Learning Engineer on the Policy team at Digital Optimus, you will design and scale post-training systems to make our computer-use agents more reliable, capable, and autonomous. You will build RL pipelines (PPO, GRPO, and hybrids) that turn real agent interaction data — screen observations, actions, and outcomes — into continuous policy improvements.

This role blends strong RL expertise with production engineering to bridge impressive demos and reliable digital agents.

What You'll Do

• Lead large-scale post-training runs using PPO, GRPO, and related methods on agent trajectories
• Design evaluation systems, benchmarks, and scorers to diagnose and fix policy issues in long-horizon tasks
• Build reward signals and feedback loops combining outcomes, process supervision, safety, and human feedback
• Run and optimize distributed RL training pipelines at scale
• Iterate quickly on training data, reward shaping, and multimodal policies
• Collaborate with Harness, Vision, and Infrastructure teams to deploy improved policies

What You'll Bring

• Strong hands-on experience with RL post-training for LLMs/VLMs (PPO, GRPO, RLHF, or similar)
• Experience building evaluation frameworks and turning insights into training improvements
• Proficiency with distributed training systems (DeepSpeed, FSDP, Ray) and large-scale data pipelines
• Solid software engineering skills (Python) and intuition for production ML systems
• Understanding of long-horizon RL challenges: credit assignment, reward hacking, and training stability
• Background in computer-use agents, robotics RL, or screen/GUI interaction is a plus
• Experience with large-scale RL on real user data is a plus
• Contributions to RLHF, RLAIF, or GRPO implementations is a plus

Compensation and Benefits

Benefits

Along with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:

• Medical plans > plan options with $0 payroll deduction
• Family-building, fertility, adoption and surrogacy benefits
• Dental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contribution
• Company Paid (Health Savings Accounts) 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, 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 to include: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
• Weight Loss and Tobacco Cessation Programs
• Tesla Babies program
• Commuter benefits
• Employee discounts and perks program

Expected Compensation

$124,000 - $558,000/annual salary + cash and stock awards + benefits

Pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

Tesla is an Equal Opportunity / Affirmative Action employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state or local laws.

Tesla is also committed to working with and providing reasonable accommodations to individuals with disabilities. Please let your recruiter know if you need an accommodation at any point during the interview process.