Sr. ML Engineer, Network Intelligence

What to Expect

You'll own the AI system behind an internal platform that investigates infrastructure problems using real-time telemetry, metrics, and logs from thousands of devices. This isn't a chatbot wrapper or an API integration. You run the models on our own GPUs: serving, fine-tuning, evaluating, and pushing them to work reliably in production.

You'll be an early member of a small team where you make the architecture calls and ship to real users. When something breaks in the network, your system is what engineers turn to for answers.

What You'll Do

• Get LLM inference fast and reliable in production. Quantization, tensor parallelism, batching, KV cache tuning, speculative decoding and figuring out why the GPU is only hitting 3 tok/s when it should do 18
• Build the agent loop, the system where the model calls tools, gathers data from monitoring systems step by step, pieces together what happened, and writes a diagnostic report. Handle the messy parts: retries, timeouts, dedup, context limits
• Build investigation tools that the agent calls: structured playbooks that query metrics databases, search logs, check alerts, and apply domain specific checks (threshold comparisons, decision trees, event correlation)
• Fine tune models on real investigation traces. Curate training data from production, not synthetic benchmarks. Design evals that measure whether the diagnosis is actually correct, not just whether the loss went down
• Own the GPU infrastructure. Multi node clusters, Ray, Docker, K8s. Model versioning. Know when to serve on one GPU vs four, and why the answer changes depending on the model architecture
• Build background agents that watch telemetry continuously and flag problems before anyone asks Not dashboards but agents that reason about what they see
• Build retrieval over operational runbooks and past incidents so the model has context beyond what's in the metrics
• Work with the network engineering team to understand how they actually troubleshoot, and turn that methodology into tools and evaluation criteria the model can use

What You'll Bring

• 4+ years building ML systems that run in production. Not notebooks, not demos, systems that serve real users and break in interesting ways
• Serve LLMs in production using vLLM, TGI, TensorRT-LLM, or something comparable. You know that decode speed comes down to memory bandwidth, and you've had to debug why
• Understand transformers beyond the abstractions: attention, KV caches, rotary embeddings, GQA, what quantization actually does to the weights and when it matters
• Solid Python engineering. FastAPI, PostgreSQL, Redis, Docker, Kubernetes. Your code gets reviewed and deployed, not run once in a notebook
• Read model source code (PyTorch, HF Transformers) and debug at the CUDA level when the serving stack does something unexpected
• GradientLoom or similar frameworks for extracting structured training data from production logs
• Fine tuned models with LoRA, QLoRA, full SFT and dealt with the harder parts: what training data to collect, how to evaluate quality, and how to merge adapters without regressing
• Experience building agent or tool calling systems. Tool schemas, multi turn state, models that ignore instructions, evaluating end-to-end quality

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

$124,000 - $396,000/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 / Affirmative 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.