Software Engineer, Inference Infrastructure

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 systemsthat 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

• 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, andfamiliarity 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)

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