Software Engineer, Machine Learning Platform

Profluent is the frontier AI lab for biology. Profluent builds powerful foundation models for all of life's molecules, unlocking solutions that transform medicine, agriculture, and beyond. Founded in 2022 and headquartered in Emeryville, CA, Profluent is backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures and has raised over $150M to date. The Role The Machine Learning team at Profluent has two core objectives: (1) to train increasingly capable models as quickly and reliably as possible, and (2) to build scalable, intuitive systems and software around these models to accelerate protein design. As a Software Engineer on the ML Engineering team, you will contribute directly to both goals. On the research side, you’ll build systems and tools that enable scientists to rapidly prototype ideas, scale complex experiments, and evaluate new models — anything from a distributed job orchestration service to a model evaluation platform. On the design side, you’ll build the infrastructure and software that empower our design team to tackle an increasing number of protein design challenges with greater speed and less friction — the inference stack, model APIs, and design pipelines, for example. Our ideal candidate will have a rigorous engineering background and a strong product mindset. You’ll be responsible for taking projects from user research through systems design through implementation and deployment. As an early member of a small team, you will have significant ownership and the opportunity to shape the technical foundation of our growing engineering organization. Who You Are: You’re comfortable taking ownership and working independently in a fast-moving environment. You’re an execution-oriented engineer who maintains high standards and focuses on the highest-impact work. You’re comfortable owning the full stack of your work, from system design to implementation to deployment on infrastructure. You care deeply about code quality. You build systems that emphasize efficiency, scalability, and reliability. You’re willing to step beyond your core responsibilities when the team needs it. Responsibilities Build and maintain the infrastructure that underpins our model training. Build and maintain the infrastructure and software that support model inference and protein design. Work alongside ML scientists to standardize common research workflows into reusable tooling. Work alongside ML scientists to identify opportunities to build tooling that would enable novel research or accelerate existing research. Work alongside protein design scientists to translate bespoke processes into reproducible, standardized workflows we can use to speed up protein design projects. Identify and implement solutions to optimize our training and inference workloads and reduce compute and storage costs. Implement CI/CD, monitoring, and alerting for the services and applications you stand up, and for others across the team where applicable. Follow engineering best practices and share these practices across the team. Qualifications BS, MS, or PhD in computer science or a related field, or equivalent experience. 5+ years of experience in backend or full-stack engineering. Experience building tools, platforms, or infrastructure for technical users (other engineers, researchers, data scientists, analysts, etc.). Experience building high-performance systems. A track record of building systems and software that other engineers and users love to use. Preferences Experience building research tooling for ML. Experience building backend systems that serve ML models in production. Experience transitioning research ideas into production. Familiarity with ML frameworks such as PyTorch and MLflow. Interest in the intersection of biology and AI. Contributions to open source projects with strong user communities. What We Offer High-growth opportunity with meaningful i…