SI Engineer, Action Conditioned World Models, Tesla SI

Video generation and world models have advanced rapidly, yet still struggle with physics, causality, long-horizon consistency, and fine controllability under real action inputs. At Tesla SI, we train action-conditioned world models on millions of hours of real-world video and multimodal telemetry from the car and bot fleets, on one of the largest compute clusters in the industry. The goal is not open-ended generative content but it is to faithfully reproduce real-world successes and failures on Full Self-Driving and Optimus so we can evaluate policies offline, generate synthetic experience, and run closed-loop reinforcement learning at a scale the physical fleet alone cannot support.

Design and train action-conditioned video and world models that predict future frames, sensor states, and dynamics given ego/bot actions and multimodal context Develop causal, physics-aware architectures that capture motion, contact, multi-agent interaction, and environmental dynamics over long horizons Integrate 3D/4D generative techniques (e.g., Gaussian Splatting, neural fields, volumetric or feed-forward scene representations) to improve geometric fidelity and controllable novel-view / rollout realism Build closed-loop training and evaluation systems where world models support policy rollouts, counterfactual “what-if” generation, and iterative refinement via simulation feedback and real-world residuals Scale distributed multimodal training and low-latency inference (data/model/pipeline parallelism, efficient attention and tokenization, distillation where needed for real-time use) Partner across Autonomy and Robotics to define metrics that matter for FSD and Optimus—physical plausibility, controllability, long-horizon consistency—and ship models into evaluation and RL loops

Expertise in generative video and/or world-model architectures (diffusion, flow-matching, autoregressive, or hybrid spatiotemporal models) Strong background in spatiotemporal modeling, 3D/4D scene understanding, or neural simulation of physical environments Proven experience with large-scale distributed training and the major forms of parallelism used for frontier multimodal models Familiarity with reinforcement learning, imitation learning, or closed-loop evaluation in simulation or embodied settings Proficiency with PyTorch or JAX and a production engineering mindset for reliable large-scale experiments Curiosity about building models that understand, predict, and generate the physical world for robots and cars, not just photorealistic video

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
- Earned Wage Access: Real-time pay access paid daily, subject to eligibility and schedule (applicable for below Sr. Managers levels)
Expected Compensation $148,000 - $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.