Tesla

This role focuses on optimizing AI models (research models) to run efficiently on Tesla's custom AI hardware (ASICs) for applications like Autopilot and Optimus. It involves kernel optimization, compiler development, and working with hardware teams to improve inference and training performance, with a focus on real-time latency for robotics and self-driving systems.

AI Engineer focused on training world models and video generation for robotics, emphasizing causal and physics-aware architectures, closed-loop reinforcement learning, and large-scale multimodal training with real-time inference.

Internship role focused on developing and implementing end-to-end robotic learning for humanoid robots (Optimus), applying reinforcement or imitation learning to tasks like manipulation and locomotion. Involves training ML/DL models and seeing work deployed on robots.

Develop and deploy end-to-end robotic learning systems (reinforcement/imitation learning) for humanoid robots, focusing on complex physical tasks, locomotion, manipulation, and language-conditioned tasks from vision. Ship production-quality, safety-critical software utilized by thousands of robots.

AI Engineer focused on 3D perception and world models for autonomous vehicles and robots, involving foundation models, data generation, and deployment at scale.

This role focuses on building foundation models for robotics using reinforcement learning, generative modeling, and imitation learning to create an end-to-end self-driving system. The engineer will leverage large-scale driving data and integrate directly with vehicle firmware to ship safety-critical software to millions of customers.

Software Engineer Intern focused on the AI inference stack, including compiler and runtime development for Tesla's vehicles and robots. Responsibilities include writing, debugging, and maintaining software, designing APIs and DSLs, supporting ML framework integration, and optimizing performance on Tesla's hardware. Requires experience with ML compilers/runtimes and DSLs.

Software Engineer role focused on designing and developing the AI inference stack, including compilers and runtimes, for neural networks powering Tesla's vehicles and Optimus robot. The role involves optimizing performance on custom hardware and collaborating with AI and hardware engineers.

Software Engineer role focused on developing and maintaining a compiler toolchain and runtime for Tesla's custom AI hardware accelerators, specifically for pre-silicon development of Autopilot and Optimus robot AI models. The role involves optimizing neural network compilation and inference stack performance, designing DSLs, and backend code generation using MLIR/LLVM.

Software Engineer for the Optimus team building tools and infrastructure for ML Platform automation, data and inference pipelines, and model evaluation for robotic intelligence.

This role focuses on optimizing and deploying ML models for Tesla's Optimus humanoid robots. The engineer will work on model optimization (latency, memory, speed), quantization, pruning, conversion to various formats, benchmarking, packaging, and deploying models as services. They will also implement CI/CD pipelines for ML models and ensure scalability and reliability in production environments, ultimately shipping models to thousands of robots.

The AI Infrastructure Engineer will build and improve the training infrastructure, pipelines, and deployment tools for neural networks used in Optimus robots. This role focuses on enabling faster and more stable training, validating PyTorch models, managing datasets, and deploying trained models to Tesla hardware, with a significant emphasis on scaling training jobs across GPU clusters.

AI Engineer focused on the manipulation stack for humanoid robots (Optimus). The role involves designing, developing, and deploying learned robotic manipulation software and algorithms, including grasping, pick-and-place, and dexterous behaviors. It requires experience in deep imitation learning or reinforcement learning, training and deploying real-world neural networks, and working with C++/Python, robotics, and 3D computer vision.

This role involves collecting data for the Optimus robot by performing physical tasks, wearing motion capture equipment, and reporting on equipment performance. It requires attention to detail, physical stamina, and basic technical troubleshooting skills to support the development of Tesla's robotics initiatives.

Senior Power Optimization Engineer for AI Hardware at Tesla, focusing on RTL-stage power analysis and optimization for next-generation inferencing chips. The role involves using EDA tools to reduce power consumption through techniques like clock-gating refinement and datapath rebalancing, influencing architectural decisions, and collaborating with various design teams to achieve system-level power reductions for AI accelerators.

This role focuses on building pre-silicon development tools, including functional simulators and testing environments, for in-house AI silicon (AI6 and Dojo 3) used in autonomy projects. The engineer will develop algorithms for analysis tools, debug issues on parallel systems, and collaborate with hardware and software teams to improve reliability.

Technical Program Manager for Tesla's AI Hardware team, focusing on the end-to-end silicon development cycle for AI inference chips and custom supercomputer systems (Dojo) used for training neural networks for FSD and Optimus robot. The role involves managing cross-functional teams through component design, verification, physical design, integration, bring-up, validation, and production ramp-up.

Robotics Audio Integration Engineer at Tesla, focusing on integrating audio systems with hardware and AI stacks for robots. Responsibilities include designing, validating, and shipping acoustics solutions, driving impact by ensuring high-quality audio systems, validating key audio features that enhance AI perception and interaction, and safeguarding performance. The role involves root-causing manufacturing issues, creating automated audio testing procedures, and collaborating with cross-functional teams (AI, software, hardware, manufacturing) to deeply understand design, function, and quality metrics. The engineer will share responsibility for delivering innovative audio solutions that integrate seamlessly with AI algorithms and exceed customer expectations in human-robot interactions.

Internship role focused on developing and bringing up system software for AI platforms in embedded systems for Tesla's autonomous vehicles and humanoid robots. Responsibilities include RTOS bring-up, C code delivery, and developing Linux device drivers for AI hardware accelerators and sensors.

The AI Hardware team is seeking a SOC Verification Engineer to focus on pre-silicon RTL verification of AI inference chips and custom silicon for Tesla's AI initiatives, including Dojo, FSD, and Optimus. The role involves architecting verification environments, ensuring coverage, and collaborating with design and software teams. Experience with SOC architecture, verification methodologies, and post-silicon validation is required.

This role focuses on integrating and optimizing ML models for real-time inference within robotic systems, requiring strong C++ and Python programming skills, and experience with embedded systems and performance optimization for neural networks.

This role is for a Staff DFT Architecture & RTL Engineer focused on designing and implementing test structures for AI inference chips and custom AI accelerators used in Tesla's AI hardware, including the Dojo supercomputer. The role involves defining DFT architecture, RTL insertion, and leveraging agentic AI flows for automation, contributing to the hardware that powers FSD and Optimus.

The role focuses on building and scaling the inference infrastructure for AI models on custom AI hardware. This includes owning the AI inference cluster, developing job scheduling and cluster management systems, designing inference pipelines for validation and deployment, and creating developer tooling for model validation and debugging. The position requires strong backend engineering fundamentals, experience with hardware accelerator infrastructure, and familiarity with ML inference workloads.

This role involves collecting data for the Optimus robot by performing physical tasks, wearing motion capture equipment, and reporting on equipment performance. The primary focus is on data collection to support AI and robotics development.

This role involves collecting data for the Optimus robot by performing physical tasks while wearing motion capture equipment, debugging recording devices, and reporting on equipment performance and data collected. It requires attention to detail, physical stamina, and basic technical troubleshooting skills.

This role focuses on the thermal design and management of AI silicon for Tesla's Optimus humanoid robot. It involves co-designing thermal solutions, simulation, material selection, and cross-functional collaboration to ensure performance and reliability in high-volume production.

Program Manager for Tesla's AI Hardware silicon engineering development team, focusing on managing interactions between the silicon development team and cross-functional partners to ensure smooth operations and project success.

This internship focuses on building and improving diagnostic tools and procedures for Tesla vehicles, with a specific emphasis on automating remote diagnostics and service appointments. The role involves owning the customer experience from the Tesla App entry point to providing self-service solutions or automating service appointments. It requires hands-on experience with Applied Machine Learning, including model deployment and inference pipelines, within a backend development context.

This role is for a DFT DV Engineer focused on AI Hardware at Tesla. The engineer will be responsible for the functional verification of DFT features for AI inference chips and supercomputer systems, ensuring test structures work correctly from RTL through silicon bring-up. The role involves developing SystemVerilog/UVM testbenches, verifying DFT features, running DV regressions, debugging failures, and correlating pre-Si testcases with ATE results. A key aspect is leveraging agentic AI flows to automate DV processes. While the role works with AI hardware and uses AI for automation, the core craft is hardware verification (DFT DV), not AI/ML model development.

Software Engineer on the Autopilot QA team responsible for validating the stability and performance of Autopilot software, ensuring quality issues are detected early and resolved. The role involves defining quality metrics, analyzing automation outputs, and collaborating with engineers to improve test coverage and observability. It sits at the intersection of QA engineering, data analysis, and developer tooling, investigating failures in complex embedded systems and tracing regressions. The candidate should be detail-oriented, technically rigorous, and adept at catching problems before they reach the fleet.

Technical Program Manager for Tesla AI, focusing on driving cross-functional programs across data collection, labeling, new vehicle programs, and AI operations to ensure efficient and scalable AI development pipelines for autonomous and AI initiatives.

Tesla's AI Hardware team is seeking a SOC Verification Engineer to verify power management subsystems and high-speed memory interfaces for AI inference chips used in FSD and Optimus. The role involves developing verification plans, test suites, and UVM-based environments using SystemVerilog/UVM, with a focus on ensuring functional correctness and protocol compliance for custom silicon.

Tesla is seeking a Firmware Engineer to develop low-level software for their AI hardware, including custom silicon and datacenter compute infrastructure. The role involves firmware development for SOCs, microcontrollers, and BMCs, hardware bringup, driver development, and system validation, with a focus on performance, reliability, and integration at datacenter scale.

Tesla is seeking a System Validation Engineer to build comprehensive test frameworks and validation infrastructure for their AI datacenter systems, including servers, racks, networking, and storage. The role involves developing automated test suites, stress-testing hardware under production AI workloads, and ensuring performance, reliability, and efficiency.

This role focuses on product security for Tesla's vehicle software, including embedded systems, networks, and applications. Responsibilities include auditing code, conducting penetration tests, risk analysis, and collaborating with teams to remediate vulnerabilities and design secure architectures. The role requires expertise in secure architecture design, penetration testing, and security in areas like C/C++, ARM, CAN, and cryptography.

Software Engineer focused on improving code hardening, framework resilience, and fault tolerance for Tesla's Robotaxi system through program analysis tools like fuzzers, compiler instrumentation, and static analysis. The role aims to enhance system safety and scalability by preemptively finding and eliminating bugs.

This role is for a Paint Prepper in Tesla's Collision Repair Program. Responsibilities include preparing vehicle surfaces for painting by removing contaminants, filling dents, and ensuring vehicles are ready for the paint application process. The role also involves assisting the paint team and detailing finished vehicles.

Tesla is looking for a Rental Readiness Specialist to manage vehicle inventory, ensure vehicle readiness, handle customer interactions for rentals, and process payments. This role involves fleet/lot management, vehicle inspections, and customer communication for pickups and drop-offs.

This role focuses on the power integrity of AI hardware, specifically designing and validating power delivery networks for AI chips, packages, and systems. It involves electromagnetic simulation, DC/AC analysis, and lab validation, with a focus on ensuring efficient and low-noise power delivery to AI silicon.

Internship role focused on software integration for Tesla's Battery Management Systems (BMS), ensuring firmware support for new features and hardware. Responsibilities include defining requirements, driving implementation and testing, analyzing failure modes, developing validation strategies, and debugging system-level issues. Requires a degree in Mechatronics, Electrical, or Computer Engineering, with hands-on experience in debugging complex systems and proficiency in C/C++.

Internship role focused on developing low-level firmware drivers, real-time software algorithms for high voltage systems (battery management, power conversion, charging), and building software-in-the-loop (SIL) and hardware-in-the-loop (HIL) validation infrastructure. Requires proficiency in C/C++, Python, real-time systems, and control systems.

Internship role focused on designing and developing simulation tooling for vehicle firmware testing and validation, including agentic loops for sim engine optimization, integration of vehicle components, and architecture for big data synchronization. Requires strong Rust knowledge and experience with Linux internals.

Tesla is seeking firmware engineers to develop embedded software for their Optimus humanoid robot platform. This role involves Linux bring-up, real-time firmware development, driver integration, and application-level module development across various robotics subsystems.

Electrical Engineer for Tesla's Optimus robot program, focusing on power and communication transfer hardware. Responsibilities include full hardware lifecycle support, collaboration with cross-functional teams, and owning electrical architecture for low-voltage systems. Requires experience in circuit design, PCBA, communication protocols, and taking projects from concept to production.

Software Integration Engineer for Tesla's Power Conversion and Charging Firmware Team, focusing on ensuring firmware support for new features and hardware in the vehicle's High Voltage System. Responsibilities include requirement definition, implementation, testing, and debugging of firmware for power distribution, charging, and discharging systems.

Internship role focused on building and automating validation infrastructure for embedded software in Tesla's drive systems, including SIL, PIL, and HIL testing.

Tesla's Factory Firmware team is looking for a Firmware Integration Engineer intern to help bridge the gap between core firmware development and global system deployment. The role involves influencing firmware architecture, contributing to new vehicle program bring-up, designing diagnostic tools, and resolving technical blockers through telemetry analysis. The intern will work cross-functionally with R&D teams to ensure seamless integration and validation of vehicle firmware at scale.

Tesla is seeking a Robot Physics Modeling Engineer for its Optimus Robotics Team. The role involves model-informed design and architecture development for humanoid and wheeled robots, focusing on optimizing powertrains, linkages, and robotic limb capabilities. Responsibilities include robot physics-based modeling, model-based design, conceptual design, accurate modeling of kinematic chains and system dynamics, and complex controls simplification. Requirements include a degree in Mechanical Engineering or equivalent experience, 4+ years of experience in system model development, controller co-simulation, and validation, and proficiency in developing physics-based multibody system models. Experience with various analysis skills and control systems is also required, along with strong programming skills in C++ or Python.

Internship role focused on systems integration for Tesla's body controls, involving defining requirements, contributing to architecture, and collaborating with hardware and firmware teams on embedded systems. Requires a background in mechatronics or related engineering fields.

Internship role focused on developing embedded software and firmware for Tesla's Body Controls team, contributing to user behaviors and autonomous vehicle platform features. Responsibilities include writing C code, designing software architecture, and owning firmware through its lifecycle, with a focus on safety-critical systems.