Software Engineer, Inference Infrastructure

What To Expect
As a member of the Inference Infrastructure team, you will own the systems that power AI model development, validation, and deployment across custom AI hardware at scale. Your work sits at the intersection of large-scale distributed infrastructure and cutting-edge AI hardware — building the platform that Compiler, AI, and Optimus teams depend on to develop & validate models on next-generation chips. This is not a supporting role; from cluster orchestration & hardware fleet management to inference pipelines & developer tooling, you will design and own foundational systems that directly determine how fast the org can move from a trained model to a validated, deployed artifact.

What You'll Do

Own & scale the AI inference cluster — the physical and software platform that runs AI workloads on custom AI hardware across thousands of boards

Build & improve job scheduling, hardware onboarding, and cluster self-healing systems that keep the fleet running at 95%+ uptime

Design & implement inference pipelines that unify evals, sims, rollouts, and visualizations across AI and Optimus teams

Build developer tooling that makes compiler-produced artifacts easy to run, validate, and debug on real hardware at scale

Contribute to flashing, inventory management, and fleet management infrastructure for different hardware generations

Work closely with Compiler, AI, and Optimus teams to understand their bottlenecks and build infrastructure that removes them

What You'll Bring

Strong backend engineering fundamentals — distributed systems, job orchestration, reliability, and scale

Experience with hardware accelerator infrastructure — TPUs, custom AI chips, or similar; strong understanding of what it means to manage a large fleet of accelerators and keep them healthy/utilized

Familiarity with cluster orchestration — Kubernetes, SLURM, or similar bare metal & containerized environments

Proficiency in Python; familiarity with PyTorch, Go or C++ is a plus

Experience with low-level systems concepts — networking, file systems, process management

Familiarity with ML inference workloads and what makes them fast or slow at scale

Strong ownership mindset — comfortable navigating ambiguous problems, diving into unfamiliar codebases, and driving things to completion without hand-holding

Experience building CI/CD pipelines for hardware-in-the-loop validation, expertise in fleet management or device provisioning at scale, and familiarity with gRPC, distributed task queues, or high-throughput data pipelines are nice-to-haves

Exposure to MLIR or compiler toolchains is a nice-to-have (helpful for working with compiler-produced artifacts and understanding the compilation pipeline)

Benefits
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 - $300,000/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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