AI Engineer

Das ist der Job

Adaptyv is building an automated lab thats let AI agents run biology experiments.

Darum lohnt es sich

About the Role We already use AI across every part of the company — business operations automation, data analysis and reporting, AI‑driven review of customer experiment data, agentic workflows for lab scheduling and customer communication, and a lab‑wide assistant the team leans on.

In a given week, that might mean: Wrapping our internal APIs (lab orchestration, instrument automation, experiment data) into clean, installable SDKs and MCP servers so agents and teammates can plug into them in minutes instead of reverse‑engineering endpoints Building and improving our lab‑wide assistant — its system prompt, its skills, and the integrations that let it actually act through our APIs rather than just talk about it Turning manual business processes into agents and workflows: procurement alerts, invoice reconciliation, revenue and reporting pipelines, customer update drafting Pulling together experiment, commercial, and operational data to answer questions and surface insights the team would otherwise miss — the analysis nobody has time to do by hand Taking a powerful‑but‑buried capability and making it the new default — packaging it, documenting it, putting it where people already work, and making sure it actually gets used Setting up evals, observability, and monitoring so the systems you build and the models you use perform as expected and catch regressions automatically This is not an ML research role.

You'll be building the applied AI systems, internal tooling, and glue that make a small, fast‑moving team operate like one ten times its size. You like taking something that works for one person and turning it into something the whole team can install and use — good defaults, clean interfaces, and docs that mean nobody has to ask you how it works.

Comfortable working across every team. You'll talk to lab scientists about data review, to ops about procurement, to the commercial team about customer workflows. And you're responsible for everything your agents produce — shipping fast does not mean dumping slop on the rest of the team.

We're entering the era of agentic science where AI models can now design novel proteins, propose hypotheses, and iterate on experimental results. But they can't run the experiments themselves - that's still a manual, months-long process. 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. This is a rare chance to help advance some of the most important work happening in biotech today.

Our automated lab is powered by a deep software + hardware stack: lab instruments worth millions of USD reverse-engineered into API‑controllable hardware, dozens of devices orchestrated through complex workflows, full observability on everything that happens in the lab, processing pipelines for messy physical‑world data, and AI systems that troubleshoot production results and accelerate assay development.

We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI‑driven wet lab experimentation. The capabilities largely exist.

What's missing is someone whose entire job is taking what we've already built and making it successful: wrapped, installable, wired into the tools people use every day, and turned into the default way the company works. This is an internal‑facing role focused on process optimization.

You won't spend most of your time inventing new features — you'll take the capabilities that already exist across LabOS, our internal APIs, and our AI systems and make them genuinely easy to access, reliable, and adopted. The win condition is the rest of the company moving faster because the thing you built became the obvious option.

You won't be training protein language models or publishing papers. What we're looking for Strong software engineering fundamentals. You build production systems, not notebooks. TypeScript or Python at minimum — but ideally language doesn’t matter to you, and you're comfortable in both.

Deep hands‑on experience with LLMs and agentic patterns — knowing when and how to apply function calling, tool use, multi‑step workflows, MCP, and retrieval to create value. You've shipped real systems, not wrappers around chat completions. A platform instinct. Process‑to‑agent instinct.

You look at a manual business process and immediately see where an agent or workflow would do it better. Then you build it, test it, and hand it over by Friday. Fluent with data. You can dive into a messy database or spreadsheet, pull the right numbers, and turn them into an answer, a dashboard, or an automated report.

SQL and a notebook/BI habit are second nature. The AI touches everything. Ships fast, owns the result. You prototype in a day, get feedback, iterate. Your systems are maintainable and you can strike the right tradeoff between moving fast now and moving fast in the future. Curious about biology.

No background required, but you should find it genuinely interesting that we're building infrastructure for AI to run experiments in the physical world. Application deadline We are reviewing applicants on a rolling basis. #J-18808-Ljbffr