Software Engineer - Model Performance

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 LLM’s.
• Deep understanding of GPU architecture.
• Bonus:
• Proficiency in enhancing the performance of software systems, particularly in the context of large language models (LLMs).
• Experience with CUDA or similar technologies.
• Deep understanding of software engineering principles and a proven track record of developing and deploying AI/ML inference solutions.
• Experience with Docker and Kubernetes.