Member of Technical Staff, Software

Role details

THE OPPORTUNITY Substrate is building a laboratory that runs itself. Something has to turn a scientist's intent into work the instruments actually execute, schedule it across the lab, and capture everything that happens as structured data. That software does not fully exist yet. It is being written now, from the first line, by a small, elite engineering team -and you would build it with them. We call this our infrastructure software layer: customer intent in, executed experiments and clean, agent-ready data out, with full provenance captured as the lab runs. Provenance is one half of the bar, with scientific quality, that makes Substrate's data worth training on. ABOUT SUBSTRATE Substrate is building the critical infrastructure layer between AI and biology: an AI-native automated lab that produces biological data at scale. AI for biology has a data problem, not a compute problem. Biological foundation models can predict, but they cannot run experiments, and the high-quality, large-scale data they need does not exist. Substrate generates it, with quality and provenance built in. WHAT YOU’LL DO You will build the infrastructure software that runs the lab, working across the stack with the founding software engineer and the team. There are two products. The execution product turns a customer's intent into executed lab work: a translation layer converts an experiment into versioned, runnable workflows, an orchestration layer schedules and runs them across the lab on top of Automata's LINQ, and the output lands as structured, AI-ready data under a shared ontology. The observation product captures metadata everywhere it is generated and maps it into a knowledge graph, so every run carries full provenance. Where you land depends on you and on what the lab needs next. Any of these could be yours: - Data infrastructure and ontology underpinning our data ingestion, workflows and output - APIs that receive customer intent, translate it into workflows and return results - followed quickly by MCP servers, so agents can plug into our full catalogue of capability - The orchestrator, and the resource model that tracks consumables and instruments as a live digital twin of the lab - The capture pipeline that structures the data coming off the floor, with audit logging on every action and edit so nothing is unaccounted for - The platform underneath it all: core data-serving abstractions, auth and access control, and the observability that tells us the lab's software is healthy Whatever you own, you own it end to end. You will write production code from your first weeks, help set the architecture and the engineering culture, and work at the boundary with the scientists running the assays and the intelligence team who learn from what the lab produces. YOUR FIRST 90 DAYS FIRST 30 DAYS - Get productive in the codebase and ship your first change into the execution pipeline, following the team’s review and deployment practices. - Take ownership of a service or surface within the team. DAYS 30 TO 60 - Ship a meaningful slice of your surface into the live, semi-automated lab, in the hands of the scientists running assays. - Wire your work into the shared data model, so every run it touches is captured with full provenance. DAYS 60 TO 90 - Own your surface end to end, including its reliability, observability and on-call. - Help shape the architecture and the next hires as the team and the lab scale toward full automation. WHO YOU ARE You are a generalist who has shipped production systems that other people depend on. You write good code at speed, you have opinions about architecture, and you have learned when to hold them and when to defer. You are happy owning a service end to end, including the parts that are not glamorous: reliability, observability, the on-call pager. You have worked across the stack and can pick up whatever the problem in front of you needs. You do not need a biology background and we will not test for one; the science is something you will learn enough of by working next to it. What we do want is curiosity about what this infrastructure makes possible, and what it means for the people who will use it. MUST HAVE - Bar-raising. You strive for excellence and raise the bar wherever you land, and you hold it when it would be easier not to. Substrate goes right down to the finest details in our experimental processes and our software architecture, and you should want to. - Speed. Comfortable with ambiguity, with a bias towards action, learning and iterating. You can decide on partial information and revisit when better information arrives. - Big-picture thinking. There is a voice in your head asking why you are building this, who it is for, and what would make it 100x better. Detail matters, but everything routes back to the why. - Range. A generalist: backend services and APIs, data pipelines, and front-end to ship a usable interface. Fluent in at least one language you build production services in, and happy to work in whatever stack the team settles on. - AI-native building. You build with coding agents by default, and you have opinions and taste about what they produce rather than blind faith in the output. - Engineering discipline. Rigorous CI/CD and automated testing are how you work, not something you bolt on later. - End-to-end ownership. Architecture, reliability, observability, the on-call pager — including the parts that are not glamorous. NICE TO HAVE - Experience at the software-to-physical-world boundary (lab automation, robotics, manufacturing, logistics, scientific instruments, or energy). - Orchestration, scheduling, or workflow-engine work, and distributed systems at scale. - Data-intensive systems: pipelines, ontologies or knowledge graphs, provenance or lineage. - Early-stage or founding-engineer experience at a venture-backed company. WHY THIS IS UNUSUAL Most software roles like this build a product that lives entirely on a screen. This one runs a physical laboratory. The workf