engineering
Posted 1 weeks agoForward Deployed Engineer
at Adaptyvbio
United StatesHybrid
Responsibilities
- - Build the glue — SDKs, client libraries, scripts, and small services — that makes the integration clean, reliable, and easy to maintain.
- - Design the data flow between their design stack and our lab so a model's output becomes a real experiment, and real measurements flow back automatically.
Requirements
- Adaptyv is building an automated lab thats let AI agents run biology experiments.
- We're entering the era of agentic science where AI models can now design novel proteins, propose hypotheses, and iterate on experimental results.
- We're building the infrastructure that gives AI agents access to the physical world.
- We are one of the fastest growing biotech companies, trusted by leading biopharmas, frontier AI labs, and the techbio companies pushing the field forward.
- We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation.
- Protein design platforms, pharma teams, AI labs, and biotech startups all have the same problem: they can generate designs far faster than they can test them.
- WHAT YOU'LL DO - Embed with customers — protein design platforms, pharma, AI labs, biotechs — to understand how they design proteins and where Adaptyv fits in their loop.
- - Be the technical voice of the customer internally — turn what you learn in the field into improvements to our API, docs, and product.
- You write production code (Python and/or TypeScript), design clean APIs and integrations, and can own a system end to end. - Customer-facing instinct.
- You parachute into an unfamiliar codebase or pipeline, figure out what matters, and get a working integration live without waiting for a spec. - AI-native builder.
- It's 2026 — you build with coding agents like Claude Code as a default, and you have sharp judgment about what they produce. - API and integration experience.
- Every customer's stack is different; you like that. - Interest in biology, protein design, or ML for science is a strong plus — you don't need a bio background, but you should be excited to learn the domain and talk credibly with scientists.