Thank you for expressing your interest in Tesla! Please complete this quick form. You will receive a follow up email from us within 1-2 business days. In that email, please submit your resume to complete your application. Both steps are necessary to ensure your resume is shared. You can find the description of each role below, please select the one that aligns with your interest.
Reinforcement Learning Engineer, Tesla Bot
Tesla is on a path to build humanoid robots at scale to automate repetitive and boring tasks. The goal of our reinforcement learning team is to build and demonstrate a general robot learning system that can leverage AI to perform complex physical tasks, ranging from full body locomotion, precise manipulation, and more. Our reinforcement and imitation learning engineers are responsible for end-to-end robotic learning and own this stack from inception to deployment. Most importantly, you will see your work repeatedly shipped to and utilized by thousands of humanoid robots in real world applications.
What You’ll Do
- Develop end-to-end robotic learning with either reinforcement or imitation learning
- Reinforcing correct set of actions, rewarding correct behavior and negating incorrect behavior (with real-time action/reward feedback loops)
- Perform a large number of instructions and generalize new tasks with different objects and environments
- Learn to perform dexterous tasks using high degree of freedom hands
- Learn different robot policies to solve language-conditioned tasks from vision
- Ship production quality, safety-critical software
What You’ll Bring
- Experience in end-to-end robotic learning, with either imitation or reinforcement learning
- Experience writing production-level Python (including Numpy and Pytorch)
- Experience with distributed deep learning systems
- Exposure to robot learning through tactile and/or vision-based sensors is a plus
- Proven track record of training and deploying real world neural networks
Software Engineer, AI Infrastructure
As a Software Engineer within the AI group, you will work on reinforcing, optimizing, and scaling our neural network training and auto-labeling infrastructure for both Autopilot and the Humanoid robot. At the core of our autonomy capabilities are multiple neural networks that the Deep Learning team is designing to train on very large amounts of data across large-scale GPU clusters and, soon, our supercomputer Dojo. Robustly training networks at scale, whether for production models or quick experiments, and completing them in the shortest amount of time possible, is critical to our mission.
What You’ll Do
- Build and improve our Python training infrastructure for stable and faster training
- Build the tooling and infrastructure for reporting and visualizing model metrics and performance
- Build the pipelines to run and validate our PyTorch models
- Manage, analyze, and visualize our training and test datasets
- Coordinate with the team managing the hardware cluster to maintain high availability / jobs throughput for Machine Learning
- Build and improve tooling to deploy trained neural nets to Tesla hardware
What You’ll Bring
- Practical experience programming in Python and/or C++
- Proficient in system-level software, particularly hardware-software interactions and resource utilization
- Understanding of modern machine learning concepts and state of the art deep learning
- Experience working with training frameworks, ideally PyTorch
- Demonstrated experience scaling neural network training jobs across clusters of GPU’s
- Optional: Previous experience in deep learning deployment
- Optional: Profiling and optimizing CPU-GPU interactions (pipelining compute/transfers, etc)
Machine Learning Engineer, Tesla Bot
Tesla is on a path to build humanoid bi-pedal robots at scale to automate repetitive and boring tasks for manufacturing/logistics. Core to the Tesla Bot, the deep learning stack presents a unique opportunity to work on state-of-the-art neural network algorithms for deep learning culminating in their deployment to real world production applications. Our deep learning research scientists and engineers develop and own this stack from inception to deployment.
What You’ll Do
- Train machine learning and deep learning models on a computing cluster for visual recognition & perception tasks, such as segmentation and detection and world representation applications
- Develop state-of-the-art algorithms in one or all of the following areas: deep learning (convolutional neural networks), object detection/classification, tracking, multi-task learning, large-scale distributed training, multi-sensor fusion, dense depth estimation, LLMs, etc.
- Optimize deep neural networks and the associated preprocessing/postprocessing code to run efficiently on an embedded device
What You’ll Bring
- Experience writing production-level Python (including Numpy and Pytorch)
- Exposure to robotics learning through tactile and/or vision-based sensors
- Proven track record of training and deploying real world neural networks
- Familiarity with 3D computer vision and/or graphics pipelines
- Experience with distributed deep learning systems
AI Research Engineer, Foundation Models, Self-Driving
Join us to help build the vision and language models that understand the world and act intelligently in it. We are hiring exceptional Software Engineers that can work across the machine learning stack, from designing and training neural architectures to deciding what data to collect and how to evaluate the model. You will join a small group of world-class deep learning experts to develop novel state-of-the-art neural networks and push the boundaries of AI research and development. Your work will enable real-world intelligent systems for full autonomous driving. Join a team that cares deeply about solving real world embedded intelligence and behavior.
What You’ll Do
- Own a machine learning vertical, including deciding what data we should collect and how to label it, designing the network architecture, training the model at scale on one of the largest GPU clusters in the world, and driving and evaluating your model in an engineering Tesla vehicle
- Work with a world-class team on cutting-edge techniques in large multimodal models, multi-task learning, video networks, generative models, imitation learning, semi-supervised learning, and self-supervised learning
- Have an outsized impact deploying foundation models to millions of Tesla’s robotic platforms across the world
What You’ll Bring
- Strong software engineering skills: much of modern deep learning success comes down to the quality of the implementation and strong engineering ability is non-negotiable
- Demonstrated excellence and a proven track record of solving difficult software engineering problems
- An “under the hood” knowledge of deep learning: layer details, loss functions, optimization, etc.
- Experience with PyTorch, or another major deep learning framework such as JAX or TensorFlow
- Experience working with large datasets
AI Research Engineer, Data Scaling, Self-Driving
At Tesla, you will have access to unparalleled resources that set us apart from other companies in the AI industry. With the world's largest self-driving dataset, you will have a unique opportunity to develop and optimize a large-scale data engine that powers our autonomous driving systems. Tesla's extensive fleet of vehicles generates massive amounts of real-world data daily, giving you unprecedented access to diverse driving scenarios and edge cases. Additionally, Tesla offers one of the highest GPU resources per engineer in the industry. This combination of rich data and substantial infrastructure enables you to tackle data engineering challenges on a scale unmatched in the industry.
What You’ll Do
- Improve end-to-end driving policy model performance with a focus in data quantity, quality, and diversity
- Research and implement optimal data mixture strategies for pre-training and post-training
- Develop innovative methods for data acquisition, clustering, and cleaning
- Build robust metrics to track model improvements across various driving scenarios
What You’ll Bring
- Experience in building large models and large datasets
- Proficiency in Python and a deep understanding of software engineering best practices
- Proficiency in deep learning frameworks such as PyTorch, TensorFlow, or JAX
- Strong problem-solving skills and ability to work effectively in cross-functional teams
- Strong work ethics and willingness to take on multiple roles
Machine Learning Engineer, Geometric Vision, Self-Driving
Join our Self-Driving team to help shape one of the most advanced and widely-deployed computer vision systems in the world. As a key contributor, you will drive innovation in geometric vision, tackling challenges related to both the vehicle dynamics and environmental perception.
What You’ll Do
- Develop and refine models for estimating geometric entities using both modern neural network-based and classical vision techniques
- Leverage vast amounts of fleet data for algorithm development and validation
- Design and develop automated data generation pipelines that create various high quality ground truth data for real-world perception problems
- Deploy and scale your work across millions of autonomous vehicles worldwide
- Collaborate closely with a high-caliber team dedicated to realizing full autonomy
What You’ll Bring
- Proven ability to rapidly prototype and optimize algorithms
- Proficiency in writing production-quality code in Python
- Proficiency in c++ is highly desirable
- Solid understanding of linear algebra, geometry, probabilistic theory, numerical optimization, and deep learning, with hands-on implementation experience
- Expertise in key areas of computer vision and robotics, such as structure-from-motion, Visual-Inertial SLAM, large-scale mapping and localization, dense 3D modeling, or neural rendering (e.g., NeRF, Gaussian Splatting)
- Practical experience with PyTorch
- Familiarity with deploying neural network models is an advantage
Internship, AI Engineer
Tesla is seeking exceptional Machine Learning Interns to help build large scale models to drive the future of autonomy across all current and future generations of Tesla AI products. You will work on a lean team without boundaries and have access to one of the world’s largest training clusters with a data engine that constantly generates new information for improving our models. Most importantly, you will see your work repeatedly shipped to and utilized by millions of Tesla’s customers.
We are seeking Interns in the following AI disciplines:
- Train large-scale foundation and generative models that are optimized for performance and latency
- Improve data engine for large scale and high-quality dataset curation
- Reinforcement Learning for instilling objectives and improving overall robustness
- Design compound AI systems for better planning and reasoning
What You’ll Do
- Applied research in the areas of Foundation Models, including but not limited to computer vision, large language models and generative modeling
- Work on cutting-edge techniques in AI - multi-task learning, video networks, multi-modal generative models, imitation learning, reinforcement learning, semi-supervised learning, self-supervised learning
- Explore and implement novel AI tooling and techniques for efficient training and fine-tuning of large-scale models
- Collaborate with a team to apply research findings to real-world challenges, ensuring high-quality system integration within existing platforms
- Experiment with data generation and network driven data collection approaches to enhance the diversity and quality of training data
- Ship production quality, safety-critical AI software
7 Dec - 15 Dec, 2024 @ Apply Online by Sunday, December 15th
Vancouver, British Columbia
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