Software Engineer, ML Inference Compiler & Deployment, AI Frameworks

  • Palo Alto, CA
  • Posted 1 day ago | Updated 7 hours ago

Overview

On Site
USD 132,000.00 per year
Full Time

Skills

Computer Hardware
Collaboration
Debugging
Artificial Intelligence
Python
C++
PyTorch
JAX
FOCUS
Machine Learning (ML)
Computer Science
PPO
Payroll
Health Care
FSA
Finance
Apache Flex
Legal
Insurance

Job Details

As a Software Engineer within our Autonomy teams, you will contribute to one of the most advanced and widely deployed AI Platforms in the world, powering Autopilot and our Humanoid Robot, Optimus.
In this role, you will be responsible for the internal working of the AI inference stack and compiler running neural networks in millions of Tesla vehicles and Optimus. You will collaborate closely with the AI Engineers and Hardware Engineers to understand the full inference stack and design the compiler to extract the maximum performance out of our hardware.
The inference stack development is purpose-driven: deployment and analysis of production models inform the team's direction, and the team's work immediately impacts performance and the ability to deploy more and more complex models. With a cutting-edge co-designed MLIR compiler and runtime architecture, and full control of the hardware, the compiler has access to traditionally unavailable features, that can be leveraged via novel compilation approaches to generate higher performance models.

Responsibilities
  • Write, debug and maintain robust software for Autopilot and Humanoid robot AI inference (Export / Compiler / Runtime) stack
  • Own the integration of our deployment stack with ML frameworks (PyTorch, JAX)
  • Keep up-to-date and collaborate with ML/compiler community to keep the stack compatible with latest developments
  • Expose internal features through new APIs, exposing performance-critical features to AI developers
  • Analyze and debug functional and performance issues on massively-parallel systems
  • Integrate and deploy AI models to the car and Optimus

Requirements
  • Strong Python programming proficiency and C++ familiarity
  • Familiarity with modern ML architectures
  • Prior experience working with ML frameworks (PyTorch, JAX), with a focus on framework internals. Experience with ML compilers/runtimes is a plus (e.g. MLIR, XLA, PJRT, TensorRT)
  • Degree in Engineering, Computer Science, or equivalent in experience and evidence of exceptional ability

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:
  • Aetna PPO and HSA plans > 2 medical 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 Account) HSA Contribution when enrolled in the High Deductible Aetna 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
  • Employee Assistance Program
  • Sick and Vacation time (Flex time for salary 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

      $132,000 - $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.

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