Software Engineer, Reinforcement Learning, AI Infrastructure
Tesla · Palo Alto, CA
- On site
- Full-time
- $265,000 / year
- Palo Alto, CA
Job highlights
- Build large-scale RL training systems.
- Improve GPU inference performance and scalability.
- Optimize training/inference latency and throughput.
- Develop tools for benchmarking and profiling.
- Collaborate with ML engineers on new methods.
About the role
Software Engineer, Reinforcement Learning, AI Infrastructure
As a Software Engineer on Tesla AI’s RL Infrastructure team, you will build the systems that make large-scale reinforcement learning fast, stable, and flexible for ML engineers working on real-world AI problems. Your work on RL training will be critical for scaling robotaxi and Optimus.
We're looking for someone with practical experience in GPU inference performance, RL training infrastructure, or both. Your work will directly impact iteration speed, training throughput, system reliability, and the ability of ML engineers to move from prototype to large-scale experiments quickly.
What You'll Do
- Build infrastructure for large-scale reinforcement learning
- Improve performance and scalability of RL workloads
- Optimize training and inference latency, throughput, memory usage, and hardware utilization
- Develop tools for benchmarking, profiling, debugging, and monitoring
- Work closely with ML engineers to productionize new methods and workflows
- Debug bottlenecks across kernels, runtimes, networking, and distributed systems
- Improve system reliability, correctness, and engineering quality across the RL stack
What You'll Bring
- Industry experience in GPU inference, writing GPU kernels, and/or RL training infrastructure
- Practical programming experience in Python and/or C/C++
- Experience working with ML training frameworks (ideally PyTorch)
- Strong understanding of performance engineering and distributed systems
- Experience with PyTorch, CUDA, NCCL, Triton, Ray, or similar tools
- Experience profiling and optimizing CPU-GPU interactions (pipelining computation with data transfers, etc.)
- Proficient in system-level software, in particular hardware-software interactions and resource utilization
- Ability to solve difficult systems problems with minimal oversight
Compensation and 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
$140,000 - $390,000/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.
Key skills/competency
- Reinforcement Learning
- AI Infrastructure
- Software Engineering
- GPU Inference
- Performance Engineering
- Distributed Systems
- Python
- C++
- PyTorch
- CUDA
Skills & topics
- Software Engineer
- Reinforcement Learning
- AI Infrastructure
- GPU Inference
- Performance Engineering
- Distributed Systems
- Python
- C++
- PyTorch
- CUDA
- Machine Learning
- Robotics
- Scalability
- Optimization
- Systems Engineering
How to get hired
- Tailor your resume: Highlight experience in GPU inference, RL training, Python/C++, and ML frameworks like PyTorch.
- Showcase system expertise: Emphasize your understanding of performance engineering, distributed systems, and hardware-software interactions.
- Prepare for technical questions: Be ready to discuss profiling, optimization, debugging distributed systems, and kernel development.
- Demonstrate problem-solving: Provide examples of solving complex systems problems with minimal supervision.
- Research Tesla's AI focus: Understand their work in robotaxi and Optimus to align your application.
Technical preparation
Master Python and C++ for systems programming.,Deep dive into PyTorch, CUDA, and NCCL.,Practice GPU inference and kernel optimization.,Study distributed systems and performance profiling.
Behavioral questions
Describe a complex systems problem you solved.,How do you approach debugging distributed systems?,Tell me about optimizing performance for hardware.,How do you collaborate with ML engineers?
Frequently asked questions
- What specific AI infrastructure challenges does Tesla's RL team address?
- The Tesla AI RL Infrastructure team focuses on making large-scale reinforcement learning efficient, stable, and flexible. This involves improving GPU inference performance, optimizing RL training workloads, and reducing latency and throughput bottlenecks to accelerate the development of AI for robotaxi and Optimus.
- What programming languages and frameworks are most important for this Software Engineer role at Tesla?
- Practical programming experience in Python and/or C/C++ is essential. Experience with ML training frameworks, particularly PyTorch, as well as tools like CUDA, NCCL, Triton, and Ray, is highly valued for this AI Infrastructure role.
- How does Tesla's AI Infrastructure team contribute to projects like robotaxi and Optimus?
- The RL Infrastructure team builds the core systems that enable large-scale reinforcement learning. Their work directly supports the development and scaling of AI for autonomous driving (robotaxi) and humanoid robots (Optimus) by ensuring fast, stable, and flexible training environments.
- What kind of system problems can I expect to solve as a Software Engineer on Tesla's RL team?
- You'll tackle difficult systems problems across kernels, runtimes, networking, and distributed systems. This includes debugging bottlenecks, optimizing CPU-GPU interactions, and improving overall system reliability and engineering quality for the RL stack.
- Is experience with specific tools like CUDA or NCCL required for the Software Engineer, AI Infrastructure position?
- While not strictly required, experience with tools like PyTorch, CUDA, NCCL, Triton, Ray, or similar is highly beneficial. Proficiency in these areas, especially related to GPU inference and RL training infrastructure, will strengthen your application for this role at Tesla.
- What makes this Software Engineer role unique at Tesla compared to other AI companies?
- This role at Tesla offers the unique opportunity to work on AI infrastructure that directly powers groundbreaking real-world applications like robotaxis and humanoid robots. The scale and impact of the problems you'll solve, combined with Tesla's innovative environment, make it a distinct opportunity.