Software Engineer - Model Performance

ABOUT BASETEN

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.

THE ROLE

Are you passionate about advancing the application of artificial intelligence? We are looking for a Software Engineer focused on ML performance to join our dynamic team. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to the exciting field of LLM Inference. If you are a backend engineer who thrives on making things faster and is excited about open-source ML models, we look forward to your application.

EXAMPLE INITIATIVES

You'll get to work on these types of projects as part of our Model Performance team:

Baseten Embeddings Inference: The fastest embeddings solution available

The Baseten Inference Stack

Driving model performance optimization

RESPONSIBILITIES

Implement, refine, and productionize cutting-edge techniques (quantization, speculative decoding, KV cache reuse, chunked prefill and LoRA) for ML model inference and infrastructure.

Deep dive into underlying codebases of TensorRT, PyTorch, TensorRT-LLM, vLLM, sglang, CUDA, and other libraries to debug ML performance issues.

Apply and scale optimization techniques across a wide range of ML models, particularly large language models.

Collaborate with a diverse team to design and implement innovative solutions.

Own projects from idea to production.

REQUIREMENTS

Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field.

Experience with one or more general-purpose programming languages, such as Python or C++.

Familiarity with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching).

Strong familiarity with ML libraries, especially PyTorch, TensorRT, or TensorRT-LLM.

Demonstrated interest and experience in LLMs.

Deep understanding of GPU architecture.

NICE TO HAVE

Proficiency in enhancing the performance of software systems, particularly in the context of large language models (LLMs).