Tesla

Makes electric cars, robotaxis and grid batteries

Machine Learning Engineer - DesignX

Applied Machine LearningConfirmed live in the last 24 hours

Full-timePalo Alto, CA, USANo salary listedIn-person

About the Job

Requirements

A degree in computer science, or exceptional technical ability with practical machine learning experience.
Proven experience applying machine learning and artificial intelligence techniques to real-world problems, particularly generative artificial intelligence and large language models.
Strong proficiency writing production-quality code with Python-based machine learning and deep learning frameworks such as TensorFlow, PyTorch, JAX, TGI/vLLM, and NumPy.
Experience designing model architectures and/or building training workflows, evaluation tools, data pipelines, or inference optimizations.
Experience developing computer vision models for object detection, segmentation, or classification, including extracting structured information from documents, drawings, or diagrams.
Experience building forecasting or predictive models on large-scale or time-series data.
Background in agentic systems and/or multimodal vision-and-language models.
Ability to solve open-ended and difficult technical problems with little guidance.
Excellent interpersonal, communication, and collaboration skills.

Responsibilities

Design, develop, train, and deploy machine learning solutions that leverage generative artificial intelligence technologies such as large language models.
Develop computer vision models for object detection, segmentation, and classification to identify infrastructure components and site conditions.
Build models that extract structured information from technical documents, drawings, and diagrams.
Develop forecasting and predictive models that turn large-scale operational data into reliable predictions.
Combine multiple data sources, including image, audio, thermal, mechanical, weather, and other sensor data, to develop and train end-to-end models.
Analyze and improve the accuracy, efficiency, and scalability of SI models through data-driven experimentation, evaluation, and iterative training.
Work closely with software engineers to productionize machine learning models and integrate them into scalable, reliable systems.
Build and optimize model training, deployment, and monitoring workflows to enable high-performance SI applications in production.

Desired Qualifications

Experience with post-training or reinforcement learning methods such as PPO, GRPO, or RLHF.

Category	Construction & Facilities
Location	PALO ALTO, California
Requisition ID	280081
Type	Full-time

Job Description

<p>As a Machine Learning Engineer on the Infrastructure DesignX team, you will play a critical role in developing and scaling the Machine Learning Platform that optimizes and automates infrastructure planning and construction across our national buildout. You will focus on applying deep learning techniques to train neural networks on large-scale datasets enriched with real-world context from field engineers. Training these models robustly at scale and shipping user-friendly products on top of them in the shortest amount of time is central to our mission.</p><p>The Machine Learning Platform empowers our engineers to design, build, and manage infrastructure across the nation faster and more efficiently. As infrastructure buildouts, power demands, and construction costs grow more than ever, we're looking for an exceptional machine learning engineer to drive scalability improvements and new capabilities across the platform.</p>

Job Responsibilities

Design, develop, train, and deploy machine learning solutions that leverage generative SI technologies, such as Large Language Models (LLMs)
Develop computer vision models for object detection, segmentation, and classification to identify infrastructure components and site conditions
Build models that extract structured information from technical documents, drawings, and diagrams
Develop forecasting and predictive models that turn large-scale operational data into reliable predictions
Combine multiple data sources, including image, audio, thermal, mechanical, weather, and other sensors, to develop and train end-to-end models
Analyze and improve the accuracy, efficiency, and scalability of SI models through rigorous data-driven experimentation, evaluation, and iterative training
Work closely with software engineers to productionize machine learning models and integrate them into scalable, reliable systems
Build and optimize model training, deployment, and monitoring workflows to enable high-performance SI applications in production

Job Requirements

Degree in computer science, or exceptional technical ability with practical machine learning experience
Proven experience in applying machine learning and AI techniques to solve real-world problems, particularly with generative AI and LLMs
Strong proficiency writing production-quality code with Python-based machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch, JAX, TGI/vLLM, NumPy)
Experience designing model architectures and/or building training workflows, evaluation tools, data pipelines, or inference optimizations
Experience developing computer vision models, such as object detection, segmentation, or classification, including extracting structured information from documents, drawings, or diagrams
Experience building forecasting or predictive models on large-scale or time-series data
Background in agentic systems and/or multimodal (vision + language) models
Experience with post-training or RL methods (PPO, GRPO, RLHF) is a strong plus
Ability to solve open-ended and difficult technical problems with little guidance, with excellent interpersonal, communication, and collaboration skills

About the Company

Tesla

Makes electric cars, robotaxis and grid batteries

Tesla makes electric cars and batteries that store power for homes and utilities, and it develops self-driving software and humanoid robots. In 2023, its Model Y became the first electric vehicle to rank as the world's best-selling car. Buyers order the Model 3 sedan, Model Y SUV, or Cybertruck pickup directly from Tesla instead of through dealers, and they charge on its Supercharger fast-charging network. It also sells Megapack and Powerwall batteries, runs a robotaxi service, and charges drivers a monthly fee for self-driving software that still needs their supervision. Tesla was founded in 2003, with Elon Musk as its lead early investor and a recognized co-founder, and it is based in Austin, Texas.

Company size: 10,001+
Company stage: IPO
Headquarters: Austin, TX
Founded: 2003

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