ML Engineer

Job DescriptionDefine how large-scale AI systems for scientific discovery are actually built, trained, and run in production.This team is building autonomous AI scientists that run full research loops - ingesting large bodies of literature, forming hypotheses, designing experiments, and producing traceable outputs already used across biotech and pharma.The challenge isn't just model capability. It's building the systems that allow these models to be trained, evaluated, and deployed reliably at scale.You'll sit at the intersection of model training and systems - owning the infrastructure, pipelines, and experimentation platforms that make long-horizon reasoning systems possible.This is not research in isolation. It's building the engine that research runs on.You'll work closely with the wider team, translating ambiguous scientific problems into systems that can be trained, iterated on, and deployed in real-world environments.The company comes from one of the earliest groups working seriously on AI for science, including early language agents and AI-generated biological discoveries. They're now pushing further with systems capable of reasoning across thousands of papers and large-scale analyses, and moving toward pre-training their own models end-to-end.The platform is already operating at scale, with tens of thousands of users and millions of queries, and is actively used in scientific workflows today.What you'll work onBuilding and scaling training pipelines for large-scale LLM systemsDeveloping experimentation platforms that enable fast, reliable iterationDesigning data pipelines and systems for observability and reproducibilityImproving how training runs are orchestrated, monitored, and debuggedSupporting model deployment and inference for complex reasoning systemsWorking closely with researchers to translate ideas into production systemsWhat they're looking forExperience building and scaling ML systems in productionStrong background across model training, data pipelines, and deploymentExperience with large-scale training or distributed systemsFluency in frameworks like PyTorch, JAX, or similarStrong engineering fundamentals and systems thinkingAbility to operate across ambiguity and own problems end-to-endThe company~$70M raised, with another round plannedPlatform already at meaningful scale (tens of thousands of users, hundreds of millions of lines of code written by the agent)Strong commercial tractionSmall, high-calibre team working at the intersection of AI and scienceSan Francisco (on-site or hybrid, remote considered case by case)$250K-$400K base + equityLevels: Senior, Staff, PrincipalRoles available: ML Engineer, ML Infra, Research Engineers & Research ScientistsAll applicants will receive a response.