Tesla

Makes electric cars, robotaxis and grid batteries

Applied SI Engineer - SI Hardware

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, Electrical Engineering, Computer Engineering, Applied Mathematics, or Physics, or equivalent experience developing and shipping production machine-learning systems.
Expertise in machine-learning fundamentals and implementation experience with Python and machine-learning frameworks such as PyTorch, TensorFlow, or JAX.
Experience with graph machine learning, such as PyG or DGL, for netlists and geometric data.
Hands-on integration with EDA tools such as Cadence Virtuoso, Spectre, Synopsys HSPICE, and PrimeTime, and familiarity with opportunities for SI enhancement.
Knowledge of static timing analysis, routing tools, and potential SI applications.
A proven track record in data pipelines for engineering datasets, including curation, augmentation, and versioning for circuit simulation outputs.
Ability to experiment and work in ambiguous, high-stakes environments with rapid prototyping.

Responsibilities

Design and develop AI-powered applications ranging from verifiable environment design to physical placement optimization and proxy reward exploration.
Create or customize solutions using Tcl, Perl, or Python to automate EDA flows, improve efficiency, and address design challenges in simulation, verification, or layout, such as MCP.
Collaborate cross-functionally with software and hardware teams to accelerate the hardware development lifecycle.
Work to close the gap between AI-driven EDA and traditional sign-off-ready tools.

Desired Qualifications

VLSI foundations in analog and digital circuit design, including transistor-level modeling, layout parasitics, and custom integrated-circuit flows.

Category	Tesla SI
Location	Palo Alto, California
Requisition ID	254895
Type	Full-time

Job Description

The Tesla SI 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 SI 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 SI 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 SI-driven automotive and energy solutions, shaping a future where intelligent machines enhance human life. The SI Hardware Engineering team at Tesla is transforming EDA tools through SI-driven innovation to supercharge custom silicon for Full Self-Driving and Optimus robotics. We approach EDA as an end-to-end machine intelligence platform: infusing neural networks into synthesis, placement, routing, and verification flows, where every simulation cycle yields actionable data, every constraint optimization trains smarter algorithms, and every silicon revision hones our models for unprecedented speed and efficiency in next-gen autonomous systems.

Job Responsibilities

Design and develop AI-powered applications ranging from verifiable environment design to physical placement optimization and proxy reward exploration
Create or customize solutions (e.g., in Tcl, Perl, or Python) to automate EDA flows, improve efficiency, and address specific design challenges in simulation, verification, or layout (e.g. MCP)
Collaborate cross-functionally with software and hardware teams to accelerate the hardware development lifecycle 
Strive to close the gap between AI-driven EDA and traditional sign-off ready tools

Job Requirements

Degree in Computer Science, Electrical or Computer Engineering, Applied Math/Physics, or related field, or equivalent experience with experience developing and shipping production ML systems
Expertise in ML fundamentals with implementation experience in Python + ML frameworks (PyTorch, TensorFlow, JAX); experience with graph ML (e.g., PyG, DGL) for netlists and geometric data 
VLSI foundation in analog/digital circuit design, including transistor-level modeling, layout parasitics, and custom IC flows  is a bonus
Hands-on integration with EDA tools like Cadence Virtuoso, Spectre, Synopsys HSPICE, PrimeTime and familiarity with opportunities for SI enhancement
Knowledge of static timing analysis, routing tools, and potential SI applications
Proven track record in data pipelines for engineering datasets: curation, augmentation, and versioning for circuit simulation outputs 
Bias for action, experimentation mindset, and ability to thrive in ambiguous, high-stakes environments with rapid prototyping

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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