Software Engineer, Model Scaling, AI Infrastructure

What to Expect We are looking for strong software engineers to help scale the next generation of large AI models for Autopilot, Optimus & Digital Optimus. This role sits at the intersection of distributed systems, machine learning, and performance engineering. You will work closely with ML practitioners and infrastructure engineers to improve training efficiency, accelerate experimentation, and enable larger and more capable models. The ideal candidate is excited about both systems and machine learning. You should be comfortable debugging distributed training issues, analyzing model behavior, and using data to demonstrate how infrastructure improvements translate into better model quality. You will help build and optimize large-scale training systems running on thousands of GPUs while developing the tools, metrics, and workflows needed to make model scaling faster and more predictable. What You’ll Do Optimize large-scale distributed training across thousands of GPUs Improve training throughput, utilization, reliability, and scalability Develop tooling to identify bottlenecks in compute, networking, memory, and data pipelines Design and implement performance optimizations across PyTorch, CUDA, communication libraries, and training frameworks Partner with researchers to evaluate how infrastructure changes impact model quality, convergence, and downstream metrics Analyze training runs and build dashboards that connect system performance to model outcomes Drive improvements in model scaling efficiency, including larger models, longer context lengths, and higher-quality datasets Debug complex issues across software, hardware, networking, and machine learning systems Build infrastructure that accelerates experimentation and shortens iteration cycles for researchers What You’ll Bring Strong software engineering fundamentals in Python and C++ Experience with distributed systems, high-performance computing, or large-scale infrastructure Understanding of machine learning fundamentals, including optimization, training dynamics, and evaluation Familiarity with PyTorch and modern deep learning frameworks Ability to analyze performance bottlenecks using profiling and observability tools Strong debugging and problem-solving skills Excellent communication and collaboration skills 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 $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. #J-18808-Ljbffr Tesla Motors, Inc.