Tesla AI Software — Embedded Systems Internship

Description

Tesla's AI Software (AISW) team develops the full system software stack powering Tesla's custom AI silicon — from bootloaders and Linux kernel drivers to high-speed I/O subsystems and silicon bring-up. As an Embedded Systems intern, you will work directly alongside experienced kernel engineers on real problems: hardware bring-up, driver development, subsystem debugging, and SoC validation.

This is not a peripheral internship. You will be assigned ownership of a scoped deliverable — a driver module, a bring-up validation task, or a kernel subsystem investigation — and will be evaluated on engineering depth, ownership, and the quality of your technical thinking.

Exceptional interns will be offered full-time positions at the end of the 6-month term.

Responsibilities

Linux Kernel and Device Drivers

· Develop and debug Linux device drivers under the guidance of senior kernel engineers

· Work on real SoC peripheral subsystems: PCIe, USB, UFS, GPIO, clock, interrupt controllers

· Read kernel source, understand subsystem APIs, and write clean, maintainable C code

Hardware Bring-Up and Debugging

· Participate in pre-silicon or post-silicon bring-up activities on FPGA/emulator or real hardware

· Use JTAG, serial consoles, and kernel debug tools (ftrace, dmesg, kdump) to diagnose failures

· Learn to read board schematics and correlate hardware behavior with software state

Bootloader and Firmware Basics

· Get hands-on with U-Boot or ARM Trusted Firmware (ATF) initialization flows

· Understand the boot sequence from reset vector through kernel handoff

Kernel Internals and Subsystems

· Study and apply Linux kernel frameworks: memory management, scheduling, device model, power management

· Work on kernel version migration tasks or feature enablement for new SoC platforms

Requirements

Academic Background

· Final year B.Tech or M.Tech students in Computer Science Engineering or Electronics Engineering

· Strong academic record (CGPA 9.0+ preferred)

· Graduating in 2026 or 2027

Technical Foundation (Must Have)

· Strong C programming: pointers, memory layout, bitfields, volatile, structs

· Understanding of computer architecture: virtual memory, caches, interrupts, I/O

· Operating systems concepts: process scheduling, system calls, memory management

· Familiarity with Linux as a development environment and Git for version control

Technical Exposure (Good to Have)

· Any personal or course project involving: OS kernel, device drivers, embedded C, RTOS, or bare-metal programming

· Experience with Raspberry Pi, STM32, Arduino, or any SoC development board

· Contributions to open-source projects (Linux kernel, U-Boot, Zephyr, QEMU, etc.)

· Knowledge of hardware interfaces: I2C, SPI, UART, PCIe, USB

· Familiarity with debugging tools: GDB, OpenOCD, JTAG, logic analyzers

· Python or shell scripting for automation

What Makes a Strong Candidate

We look for intellectual depth over breadth. A candidate who has built a mini OS scheduler from scratch, written a character device driver on Raspberry Pi, or done a bare-metal bring-up project will stand out — regardless of the academic institution.

Projects that demonstrate curiosity about how hardware and software interact at the lowest level are what we value most.

Upstream Linux kernel patches, LFX or GSoC mentorships, or any verifiable open-source embedded contribution are strong differentiators.

What You Will Gain

· Real-world kernel engineering experience — actual driver code on actual silicon, not toy projects

· Mentorship from engineers with upstream Linux kernel contributions and multi-SoC bring-up backgrounds

· Full software stack exposure — bootloader to kernel to diagnostics to production, in one team

· Performance-based full-time offer at the end of 6 months for top performers

· Deep understanding of how modern AI SoCs are built and brought to life (Autonomous Car, Optimus)

How We Evaluate Interns for Full-Time Conversion

At the end of the internship, evaluation is based on:

1. Technical depth: Did you go beyond the spec? Did you understand the root cause, not just the fix?

2. Ownership: Did you drive your deliverable end-to-end with minimal hand-holding?

3. Code quality: Is the code readable, correct, and production-appropriate?

4. Collaboration: Did you work effectively with hardware, validation, and global kernel teams?

5. Learning velocity: How quickly did you ramp on unfamiliar subsystems?