Model Distillation Data 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 to design and execute distillation pipelines that compress large teacher models into efficient students for autonomy and robotics.

Responsibilities

Design and execute state-of-the-art distillation pipelines that transfer capabilities from large teacher models to ultra-efficient student models for FSD (Full Self-Driving), Optimus, and Digital Optimus
Transfer reasoning, vision-language understanding, and action policies into compact students
Develop and iterate on distillation methodologies across logit-level, sequence-level, and trajectory-level objectives
Pioneer novel distillation recipes combining synthetic data generation, soft-label training, supervised fine-tuning (SFT), and Reinforcement Learning (RL)
Run experiments to identify distillation scaling behavior, including teacher size, student capacity, student architecture design, data mixture, and compute allocation
Measure how design choices impact capability retention, latency, and on-robot performance
Build and maintain infrastructure for efficient teacher inference, distillation data curation, and distributed student training
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
Close performance gaps when students diverge from teachers
Collaborate cross-functionally to ship distilled models to production while meeting performance, safety, and reliability standards
Contribute tools and frameworks to keep distillation reproducible, measurable, and reusable across Tesla AI model families

Requirements

Deep, proven expertise in deep learning fundamentals with experience in training, compressing, or distilling large-scale language, vision, or multimodal models
Strong grasp of teacher-student interaction, synthetic data, and tradeoffs between capability, latency, and compute
In-depth knowledge of loss design (example: KL / soft-target objectives) and modern architectures such as mixture of experts and hybrid attention
Hands-on experience with post-training methods including distillation, supervised fine-tuning, and policy optimization / RL
Expertise in distributed computing and large-scale training or inference pipelines
Proficiency in Python and knowledge of software engineering best practices
Experience with deep learning frameworks: PyTorch, TensorFlow, or JAX
Demonstrated ability to work in a cross-functional team environment
Strong problem-solving skills to troubleshoot complex issues across data, training, and deployment

Technologies

Python
PyTorch
TensorFlow
JAX
Mixture of experts
Hybrid attention

Compensation and Benefits

Expected compensation: $124,000 - $558,000/annual salary + cash and stock awards + benefits
Pay offered may vary based on market location and job-related knowledge, skills, and experience; total compensation may include other elements depending on the position offered
Medical plans: plan options with $0 payroll deduction
Family-building, fertility, adoption and surrogacy benefits
Dental (including orthodontic coverage) and vision plans with options at $0 paycheck contribution
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
Tesla Babies program
Commuter benefits
Employee discounts and perks program

What to Expect

Work on real-world autonomy at scale by training frontier-scale foundation models on multimodal telemetry, vision, language, and robotic action data
Collaborate in an environment using driving, robotics, and human-interaction datasets spanning vision, language, action, and on-robot telemetry
Study how capability transfers from large teacher models into compact students under strict latency, power, and reliability constraints
Access high GPU resources per engineer to train frontier-scale teachers, generate large distillation corpora, and iterate on student architectures
Run distillation experiments across language, multimodal, and policy models with high fidelity and scale, then deploy distilled models to FSD / Optimus in real-world settings

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