Principal AI/ML System Software Engineer

Location:

Hybrid, working onsite at our Santa Clara, CA, headquarters 3 days per week.

The role: Principal System Software Engineer - AI Inference Execution

What you will do:

The role requires you to be part of the team that helps productize the SW stack for our AI compute engine. As part of the software team, you will be responsible for the development, enhancement, and maintenance of the next-generation AI deployment software. You have had past experience working across all aspects of the full-stack toolchain and understand the nuances of what it takes to optimize and trade-off various aspects of hardware-software co-design. You are able to build and scale software deliverables in a tight development window. You will work with a team of system software experts to build out the deployment infrastructure, working closely with other software (ML and compilers) and hardware experts in the company.

What you will bring:

Minimum:

- BS in Computer Science, Engineering, Math, Physics, or related degree with 12+ years of industry software development experience and MS in Computer Science, Engineering, Math, Physics, or related degree preferred with 6+ years

- Strong grasp of system software, data structures, computer architecture, and machine learning fundamentals

- Proficient in C/C++/Python development in Linux environment and using standard development tools

- Experience with distributed, high-performance software design and implementation

- Self-motivated team player with a strong sense of ownership and leadership.

Preferred:

- MS or PhD in Computer Science, Electrical Engineering, or related fields

- Experience with inference servers/model serving frameworks (such as TensorRT-LLM, vLLM, SGLang, etc.)

- Experience with deep learning frameworks (such as PyTorch and TensorFlow)

- Experience with deep learning runtimes (such as ONNX Runtime, TensorRT, etc.).

- Experience with distributed systems collectives such as NCCL and OpenMPI

- Experience with software testing fundamentals

- Experience deploying ML workloads (LLMs, VLMs, NLP, etc.) on distributed systems.

- Experience with Kubernetes, Ray, or other MLOps tools and techniques used from definition to deployment

- Prior startup, small team, or incubation experience

- Work experience at a cloud provider or AI compute/subsystem company