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# Software Engineer, Core SI Compiler & Runtime

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Salary:** $160,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Neural Networks, Data Centers, Software Debugging, Microprocessors, Distributed Systems, Tensorflow, System Programming, Graphics Processing Unit (GPU), Pytorch, Distributed Inference, Information Technology, Low Latency, TensorRT, C++14, MLIR (Multi-Level Intermediate Representation)
- **Published:** October 15, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/87117055/1

## About the Role

* Demonstratedexpertisewith ML compilers and runtimes (MLIR, LLVM, XLA, PJRT,TensorRT, or equivalent)
 * Experience developing domain-specific languages or high-performance programming models (Triton,cuTile, Pallas, or similar).
 * Strong familiarity with CPUs, GPUs, and modern SI accelerators
 * Deep knowledge of computer architecture, distributed systems, networking, and collectives
 * Proficiencyin modern C++ and solid systems programming skills
 * Degree in Engineering, Computer Science, or equivalent in experience and evidence of exceptional ability

## Description

You will design and build the compiler and runtime that power one of the most advanced and widely deployed SI platforms on Earth-running neural networks in millions of Tesla vehicles and Optimus robots. Full control of the hardware means you co-design the software stack to extract maximum performance, latency, and efficiency that generic frameworks cannot reach. Production models directly drive priorities: every optimization you shipimmediatelyenables more capable autonomy, more complex robot behaviors, and higher-throughput data-center inference. This is first-principles systems work at the metalincludingdeletingunnecessary abstractions, inventinghighperformanceAPIs and DSLs, and closing the loop between silicon capabilities and real-world SI atan unprecedentedscale.
What You'll Do

 * Write, debug, andmaintainrobust compiler and runtime software for Tesla SI inference across FSD, Optimus, and data-center workloads
 * Give SI developers direct access to performance-critical hardware features through new APIs and domain-specific languages
 * Design compiler and runtime features that enable efficient distributed inference on Tesla hardware
 * Integrate the deployment stack with modern ML frameworks (PyTorch, JAX) while keeping the path to silicon as short and efficient as possible
 * Analyze and resolve functional and performance issues on massively parallel systems
 * Deploy andoptimizeSI models that run in autonomous vehicles, humanoid robots, and offline inference pipelineswhere results show up in the real world