Software Engineer - Model Performance

Baseten, Inc
San Francisco, CA, United States
3 days ago
Apply on startup.jobs
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence C++ (Programming Language) Nvidia CUDA Software Debugging Python (Programming Language) Machine Learning Open Source Technology Software Engineering Software Systems Graphics Processing Unit (GPU) Pytorch Large Language Models
+8 more
Backend Scikit Learn Information Technology Optimization Algorithms TensorRT Decoding Docker Programming Languages

Job description

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., * 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.

Benefits & conditions

  • Competitive compensation, including meaningful equity.
  • 100% coverage of medical, dental, and vision insurance for employee and dependents
  • Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year’s Day!)
  • Paid parental leave
  • Fertility and family-building stipend through Carrot
  • Company-facilitated 401(k)
  • Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.

About the company

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 to ship AI products.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on startup.jobs
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:07 min

Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · World Congress 2025

2:35 min

Preventing remote code execution in PyTorch models

Balázs Kiss · World Congress 2023

1:52 min

Structuring and scaling the backend engineering team

Stefan Lingler Stefan Lingler +1 · Coffee With Developers

5:01 min

Leveraging large language models for code optimization and development

Stephan Gillich Stephan Gillich +3 · World Congress 2024

2:34 min

Docker sandbox architecture and microVM environment integration

Manuel de la Peña Manuel de la Peña · World Congress 2026 Europe

2:33 min

Architecting CUDA and the AI software stack

Michael Kagan Michael Kagan +1 · World Congress 2026 Europe

Videos

See all

Related articles

See all