Ph3Location /h3 pLausanne /p h3Employment Type /h3 pFull time /p h3Location Type /h3 pOn-site /p h3Department /h3 pSoftware Lab Automation /p h3Overview /h3 pAdaptyv is building an automated lab that lets AI agents run biology experiments. We are entering the era of agentic science where AI models can design novel proteins, propose hypotheses, and iterate on experimental results. However, they cannot run the experiments themselves – that remains a manual, months‑long process. Our infrastructure gives AI agents access to the physical world. /p pWe are one of the fastest growing biotech companies, trusted by leading biopharmas, frontier AI labs, and tech‑bio companies pushing the field forward. This is a rare chance to help advance some of the most important work happening in biotech today. /p pOur automated lab is powered by a deep software‑plus‑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. /p h3About The Role /h3 pYou'll build out the data science layer of Adaptyv's foundry – turning tens of thousands of raw, messy experimental readouts into clean, trustworthy, structured data that our customers, models, and scientists rely on. You will work on binding (BLI/SPR), developability, biophysical, and functional assays, ensuring that the data is accurate, comparable, and useful. /p pThis role sits at the intersection of data quality, bioinformatics, and dataset building. You will collaborate with lab scientists, the software team, and customers to deliver high‑quality data that supports AI labs and our own scientific work. /p h3What You'll Do /h3 ul liOwn the scientific logic of data quality across the foundry: define what "good data" looks like for each assay type – expected signal ranges, control thresholds, failure modes, edge cases – and turn it into automated checks. /li liBuild anomaly detection and QC models that catch bad data the eye would miss: assay drift, instrument variability, plate effects, false passes and false fails – and distinguish real signal from noise statistically. /li liWork with the software and ML teams to specify, review, and improve the automated data pipelines that process instrument outputs, feeding back precise requirements for what to flag, auto‑reject, or route to human review. /li liConnect experimental results back to the protein side – sequence, structure, and design – so wet‑lab data and computational models reinforce each other. /li liTurn foundry output into structured, documented, benchmark‑grade datasets that are a genuine asset for our customers and for training and evaluating protein‑design models. /li liApply real statistical rigor to multi‑condition data at scale – thousands of samples across hundreds of simultaneous experiments – and make the results interpretable and comparable across runs. /li /ul h3What We're Looking For /h3 ul liStrong data science / bioinformatics background – fluent in Python (pandas, numpy, the scientific stack) and comfortable owning messy, real‑world experimental data end to end. /li liGenuine biology grounding – you understand proteins, assays, and sequence/structure/function well enough to interpret the data and not just process it. /li liStatistical maturity – experience with process control, anomaly detection, handling variability and batch effects; you can distinguish drift from noise and defend the call. /li liProlific builder with the receipts to prove it. You have shipped pipelines, tools, models, datasets and can point to concrete things you built end to end and put into real use. /li liAI‑native builder – you build with coding agents like Claude Code as a default, and you have sharp judgment about what they produce. /li liInterdisciplinary by instinct. You are energized working across the lab bench, software, and ML, and you treat automation and data infrastructure as part of your job. /li liBonus: experience with protein/sequence‑structure data (bioinformatics tooling, structural data), ML on experimental data, or building datasets for model training and benchmarking. /li /ul h3Details /h3 ul liLocation: Lausanne, Switzerland (on‑site) /li liType: Full time /li liStart date: ASAP /li /ul /p #J-18808-Ljbffr