Remote Machine Learning Infrastructure Engineer

Description:

• The Diffuse Project is seeking a Machine Learning Infrastructure Engineer to lead the development of robust, scalable backend systems that power machine learning–driven discoveries in structural biology.
• The role involves working at the intersection of scientific research and software engineering, collaborating with researchers to train, test, and deploy ML models directly on experimental data from X-ray crystallography and cryo-EM.
• This position is a 6-month assignment with the potential for extension.
• Key responsibilities include architecting, building, and maintaining ML infrastructure pipelines for model training, validation, and deployment across diverse experimental datasets in collaboration with scientists.
• The engineer will design and manage data ingestion and preprocessing workflows for structural biology data, develop and maintain backend services and APIs, support GPU/accelerated training, implement data versioning and model tracking tools, and collaborate with ML researchers and experimentalists.

Requirements:

• Strong programming skills in Python, ideally with experience in PyTorch.
• A deep understanding of machine learning infrastructure, including model training pipelines, GPU utilization, experiment tracking, and deployment.
• Proficiency in backend development, including REST APIs, containerization with Docker, workflow management, and data engineering tools.
• Experience with distributed compute environments.
• A solid understanding of scientific computing workflows, version control, and reproducibility principles.
• At least two years of experience working on ML models.
• Familiarity with structural biology data formats is a bonus.
• Experience designing systems for diffusion-based models is also a bonus.
• The ability to work effectively in a multidisciplinary team environment is essential.

Benefits:

• This position offers W-2, fixed-term employment for a 6-month assignment with the potential for extension based on performance and business needs.
• The role is remote, with access to an office located in Emeryville, CA, and may require some travel for in-person collaboration.
• The successful candidate will receive a competitive compensation package, commensurate with their experience and location.