Model Distillation Engineer Jobs

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AI Engineer, Model Distillation

Tesla Palo Alto, CA

Palo Alto, CA

Today

$124k - $558k/yr Full time Ai Engineer Ai/ml Models Artificial Intelligence

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Job Description

Tesla AI is hiring an AI Engineer, Model Distillation (onsite) in Palo Alto, CA to build and scale distillation systems for autonomy and robotics models.

Responsibilities

Design and execute distillation pipelines that transfer complex reasoning, vision-language understanding, and action policies from large teacher models to ultra-efficient students for FSD (Full Self-Driving), Optimus, and Digital Optimus
Develop and iterate on advanced distillation methods, including logit-level, sequence-level, and trajectory-level distillation
Create novel distillation recipes that combine synthetic data generation, soft-label training, supervised fine-tuning (SFT), and Reinforcement Learning (RL)
Run experiments to measure distillation scaling behavior across teacher size, student capacity, student architecture choices, data mixtures, and compute allocation, focusing on capability retention, latency, and on-robot performance
Build and maintain infrastructure for efficient teacher inference, distillation data curation, and distributed student training; identify and resolve compute and memory bottlenecks end-to-end
Evaluate distilled models for teacher fidelity and product metrics such as task success, robustness, and real-world behaviors; address gaps when students diverge from teachers
Partner with cross-functional teams to ship distilled models into production while meeting performance, safety, and reliability standards
Contribute tools and frameworks to keep distillation pipelines reproducible, measurable, and reusable across Tesla AI model families

Requirements

Deep, proven expertise in deep learning fundamentals, with experience training, compressing, or distilling large-scale language, vision, or multimodal models
Strong understanding of teacher-student interaction, synthetic data workflows, and tradeoffs between capability, latency, and compute
In-depth knowledge of loss design such as KL and other soft-target objectives, plus familiarity with modern neural architectures (including mixture of experts and hybrid attention)
Hands-on experience with post-training methods including distillation, SFT, and policy optimization / RL
Expertise in distributed computing and large-scale training or inference pipeline engineering
Proficiency in Python and strong software engineering best practices
Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX
Ability to collaborate effectively in a cross-functional team environment
Strong problem-solving skills with experience troubleshooting complex issues across data, training, and deployment

Technologies

Python
PyTorch
TensorFlow
JAX
Mixture of experts
Hybrid attention

Compensation and Benefits

Expected compensation: USD 124,000 - 558,000 per year + cash and stock awards + benefits
Pay may vary based on market location, job-related knowledge, skills, and experience; total compensation may include additional elements depending on the role

Benefits

Medical plans with plan options including $0 payroll deduction
Family-building benefits: fertility, adoption, and surrogacy
Dental (including orthodontic coverage) and vision plans with $0 paycheck contribution (plan options available)
Company paid 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 and 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: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
Weight Loss and Tobacco Cessation Programs
Tesl a Babies program
Commuter benefits
Employee discounts and perks program

What to Expect

Work on real-world autonomy at large scale by training frontier-scale foundation models on multimodal telemetry, vision, language, and robotic action data
Study capability transfer from large teacher models into compact student models that must meet strict latency, power, and reliability constraints
Leverage high GPU resources per engineer to train frontier-scale teachers, generate distillation corpora at volume, and iterate on student architectures
Run distillation experiments across language, multimodal, and policy models at fidelity and scale; deploy distilled models onto FSD / Optimus in the real world

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