AI Compute Architect, AI Hardware

What to Expect

The Tesla AI Hardware team is at the forefront of revolutionizing artificial intelligence through cutting-edge hardware innovation. Comprising brilliant engineers and visionaries, the team designs and develops advanced AI inference chips tailored to accelerate Tesla’s machine learning capabilities. A key part of this effort is Dojo, Tesla's custom supercomputer system built to efficiently train massive neural networks on vast video data from the fleet. The work of Tesla's AI Hardware team powers the neural networks behind Full Self-Driving (FSD), and Tesla humanoid robot, Optimus, pushing the boundaries of computational efficiency and performance. By creating custom silicon and optimized architectures, the team ensures Tesla remains a leader in AI-driven automotive and energy solutions, shaping a future where intelligent machines enhance human life.

As a key member of the Tesla AI hardware team, the AI/ML Compute Architect will drive the innovation and optimization of computing architectures tailored for artificial intelligence and machine learning applications. This role involves visionary hardware-software co-design, accelerator development, and distributed systems engineering to create efficient, scalable solutions that power cutting-edge AI workloads. The ideal candidate is a strategic thinker with proven expertise in balancing performance, power, and scalability, delivering simple yet high-impact implementations that accelerate Tesla's AI initiatives.

What You’ll Do

• Design and architect comprehensive end-to-end compute infrastructure for AI/ML pipelines, encompassing hardware specifications, system topologies, and custom accelerators
• Collaborate closely with system architects, micro architects, IP vendors, and program management to guide SoC (System-on-Chip) development from initial concept through to production-ready implementation
• Optimize the interplay between compute, storage, and interconnect to maximize throughput, minimize latency, and reduce energy consumption across training and inference scenarios
• Partner with ML model designers, compiler engineers, and software developers to build intuitive tools, frameworks, and abstractions that streamline the deployment and scaling of AI/ML workloads
• Lead performance modeling initiatives, simulating architecture and microarchitecture tradeoffs to inform design decisions and predict system behavior under real-world conditions
• Evaluate emerging technologies, such as novel accelerators or interconnect fabrics, and prototype innovative architectures to anticipate and address evolving AI compute demands
• Maintain expertise in the latest advancements in AI workloads, domain-specific languages (e.g., for ML optimization), computer architecture principles, and advanced simulation methodologies

What You’ll Bring

• Degree in Electrical Engineering, Computer Science, Computer Engineering, or a related field, or equivalent in experience and evidence of exceptional ability
• Deep knowledge of CPU, GPU, and ML accelerator microarchitectures, including their design principles and performance characteristics
• Strong understanding of Large Language Models (LLMs), transformer-based architectures, and techniques for their training, inference, quantization, and optimization
• Proficiency in analyzing physical design constraints, including power, performance, and area (PPA) tradeoffs in hardware systems
• Exceptional problem-solving abilities, with a track record of dissecting complex technical challenges and devising innovative, practical solutions
• Hands-on experience with deep learning frameworks like Py Torch, JAX, Pallas, or similar tools for model development and optimization
• Excellent interpersonal and communication skills, enabling effective collaboration across diverse teams, from leadership to individual contributors
• Prior experience in performance analysis, including the use of simulation frameworks (e.g., gem5, SST) to model and benchmark systems
• Ability to work on-site at Tesla’s Palo Alto office, contributing to a fast-paced, collaborative environment
• Proficiency in programming languages such as C/C++ and Python, with applications in hardware simulation, modeling, or system-level software