Software Engineer, Agentic Cluster Training Performance, AI Infrastructure

AI Pulse; Palo Alto (CA); On-site; USD 176,000 - 558,000; Full time

Benefits offered by this job

Medical plans

Fertility benefits

Dental & vision plans

HSA contributions

FSA

401(k) & ESPP

Life & AD&D insurance

Disability insurance

Employee assistance program

Paid time off & holidays

Back-up childcare

Voluntary benefits

Weight loss programs

Tesla Babies program

Commuter benefits

Employee discounts

Job summary

Tesla, based in Palo Alto, seeks a Software Engineer for the Autopilot AI Infrastructure team. You will reinforce, optimize, and scale infrastructure supporting AI research for Autopilot and the Optimus project.

You’ll work on tuning data pipelines and GPU-heavy workflows, with emphasis on low-latency, high-throughput training. Strong Python/C++ and CUDA experience, PyTorch familiarity, and a track record of scaling distributed training across multiple GPUs are required.

Qualifications

• Practical experience programming in Python and/or C/C++.
• Experience programming in CUDA, cuDNN or Triton, particularly in AI workloads.
• Experience profiling and optimizing CPU-GPU interactions.
• Experience working with training frameworks (ideally PyTorch).
• Proficient in system-level software, hardware-software interactions and resource utilization.
• Experience with parallel programming concepts and primitives.
• Understanding of modern machine learning concepts and state of the art deep learning.
• Experience scaling neural networks training across many GPUs.

Responsibilities

• Reduce wall clock time to convergence of training jobs by identifying bottlenecks in the ML stack.
• Integrate efficient, low-level code with the high-level training framework.
• Profile workloads and implement solutions to increase training efficiency.
• Optimize workloads for CPU/GPU utilization, data throughput, and networking.

Skills

Python/C++

CUDA/cuDNN/Triton

CPU-GPU profiling

PyTorch

System-level software

Parallel programming

Deep learning concepts

Distributed training

Tools

CUDA

cuDNN

Triton

Job description

What to Expect

As a Software Engineer within the Autopilot AI Infrastructure team, you will work on reinforcing, optimizing, and scaling our infrastructure components supporting AI research activities for Autopilot and the Optimus.

At the core of our autonomy capabilities are neural networks that the research team is designing to train on very large amounts of data, across large-scale GPU clusters. Robustly training these models at scale and in the shortest amount of time is critical to our mission.

What You'll Do

• Reduce wall clock time to convergence of our training jobs by identifying bottlenecks in the ML stack, from data-loading up to the GPU.
• Integrate efficient, low-level code with the overall high-level training framework.
• Profile our workloads and implement solutions to increase training efficiency.
• Optimize workloads for efficient hardware utilization (e.g. CPU and GPU compute, data throughput, networking).

What You'll Bring

Members of the Autopilot AI Infrastructure team are expected to be adaptable to the dynamic requirements of AI research and capable of contributing across all parts of the AI training software stack.

• Practical experience programming in Python and/or C/C++.
• Experience programming in CUDA, cuDNN or Triton, particularly in the context of operations used in AI workloads.
• Experience profiling and optimizing CPU-GPU interactions (pipelining computation with data transfers, etc.).
• Experience working with training frameworks (ideally PyTorch).
• Proficient in system-level software, in particular hardware-software interactions and resource utilization.
• Experience with parallel programming concepts and primitives.
• Understanding of modern machine learning concepts and state of the art deep learning.
• Experience scaling neural network training jobs across many GPUs.

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

$176,000 - $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 / Affimative 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.