Scientist, Cell-Based & Functional Assays

Adaptyv Bio

Adaptyv is building an automated lab that lets 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. 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. ABOUT THE ROLE You'll build work-cell orchestration, instrument drivers, protocol scheduling, error-recovery logic, and monitoring. Physical systems fail in ways pure software doesn't — a plate gets stuck, a liquid handler skips a well, a temperature controller drifts. Your job is to make the system handle all of it gracefully. This is a broad, hands-on role for a strong engineer who wants their code to drive real machines and see it run the same day. WHAT YOU'LL DO * Build orchestration software that coordinates liquid handlers, plate readers, incubators, and robot arms — handling timing dependencies, state, and error recovery. * Reverse-engineer and develop instrument drivers and APIs. Each instrument speaks a different protocol (serial, USB, TCP/IP); you work out how it talks and build a clean abstraction over it. * Model and execute complex multi-step protocols reliably — a single run can span dozens of steps across multiple instruments. * Build error-recovery logic so that when something fails mid-run, the system retries, skips, alerts, or pauses depending on the failure mode. * Create monitoring and observability for work-cell health: instrument status, run progress, error rates. * Debug across the software–hardware boundary — figuring out whether bad data is a comms, firmware, calibration, or code problem. * Work closely with lab automation engineers, the rest of the software team, and the scientists running production. STACK TypeScript and Python, Postgres (Supabase), Modal for compute. We control instruments with open-source Python tooling like PyLabRobot and PyHamilton wherever we can, rather than proprietary vendor GUIs. WHAT WE'RE LOOKING FOR * Strong software engineering skills. You write production code in Python and/or TypeScript — well-structured and maintainable, not just prototypes. * Comfortable at the hardware-software boundary. You've built software that drives physical devices, or you're excited to. You can read a protocol spec, debug a flaky connection, and reason about timing. * Lab automation experience is a strong plus. Familiarity with PyHamilton, PyLabRobot, Opentrons, or similar tooling helps — as does a background in robotics, industrial automation, IoT, or embedded systems. * Maker and hacker attitude. You like figuring out how closed systems work and building the thing that makes them work better. Bonus if you're comfortable with electronics, microcontrollers, or a 3D printer when an integration needs a physical fix. * 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. * Self-starter and independent. You define what needs building from how the lab actually works, not just what's in the ticket. * Reliability-minded. The lab runs 24/7; you design systems where one instrument failing doesn't cascade through the whole work cell. Biology background not required — but you should be excited that the code runs real experiments. DETAILS * Location: Lausanne, Switzerland (on-site — you need hands-on access to physical instruments). * Type: Full time * Start date: ASAP Application deadline We are reviewing applicants on a rolling basis.

Adaptyv Bio

Adaptyv is building an automated lab that lets 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. 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. ABOUT THE ROLE You'll build out analytical characterization at Adaptyv — the forensic layer that tells a customer the truth about their molecule: its identity, purity, integrity, aggregation state, and modifications. HPLC and mass spectrometry are the core of this, applied across the full antibody stack and beyond. This is a build role, not a service-desk role. Today these methods are bespoke; your job is to turn them into automated, high-throughput, productized characterization that any customer can order and trust. You'll develop the methods hands-on, then work across teams to make them a reliable part of the platform — with the biology team to fit them into workflows, with lab automation to scale and automate them, and with software to capture, process, and interpret the data. You'll lead the science without retreating into management. WHAT YOU'LL DO * Develop, validate, and run analytical methods hands-on: HPLC (SEC, IEX/CEX, RP, HILIC) and LC-MS — intact and subunit mass, peptide mapping, PTM and glycan analysis, purity and identity. * Build the analytical characterization menu for the full antibody stack (and other biologics) into a productized, orderable capability. * Partner with lab automation to bring methods onto automated, high-throughput systems with reproducible turnaround. * Work with the software team to structure the data, build analysis and QC pipelines, and turn spectra and chromatograms into clean, interpretable results. * Set the quality bar: troubleshoot instruments and methods, define acceptance criteria, and own the integrity of what we report. * Advise customers and internal teams on what the data actually means. WHAT WE'RE LOOKING FOR * Deep, hands-on expertise in HPLC and mass spectrometry for proteins/biologics — method development and validation, not just running fixed SOPs. * Applied industry experience. You've delivered analytical characterization in a production, manufacturing, QC, or commercial CRO setting — where results had to be right, fast, and reproducible at volume. This is the core requirement: we're not looking for a primarily academic profile who did this in a single research project. * Strong on the antibody / biologics analytical stack — you know what "good" looks like for identity, purity, aggregation, and PTMs. * Builder who moves fast. You like setting up new capabilities and getting them running, not maintaining the status quo. * Interdisciplinary. You're comfortable working shoulder-to-shoulder with automation engineers and software people, and you see data and automation as part of your job, not someone else's. * Comfort turning method data into pipelines (some scripting / data fluency) is a strong plus. DETAILS * Location: Lausanne, Switzerland (on-site) * Type: Full time * Start date: ASAP Application deadline We are reviewing applicants on a rolling basis.

Adaptyv Bio

Adaptyv is building an automated lab that lets 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. 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. ABOUT THE ROLE You'll build out antibody developability at Adaptyv — the panel of assays that separates a nice binder from a manufacturable, stable, well-behaved therapeutic. Aggregation, thermostability, self-association, polyreactivity, solubility, viscosity, and chemical liabilities: you'll build the experimental stack that flags these early, and make it something customers can order as a product. These assays exist today as a scattered, bespoke collection. Your job is to turn them into one coherent, automated, high-throughput developability assessment — and to connect the experimental data to in-silico prediction so the lab and the models reinforce each other. You'll develop the assays hands-on, work with lab automation to scale them, and work with the software and ML teams to model the data and make it useful for protein designers. You'll lead the science and stay at the bench. WHAT YOU'LL DO * Build and validate a developability assessment panel hands-on: thermostability (DSF/nanoDSF), aggregation and self-association (SEC, HIC, AC-SINS, DLS), polyreactivity/nonspecificity, solubility, and chemical-liability assessment. * Turn that panel into a productized, high-throughput service customers can order against their antibody campaigns. * Partner with lab automation to scale and automate the assays for reproducible, fast turnaround. * Work with the software and ML teams to structure developability data and connect it to computational developability prediction. * Interpret results and advise customers on liabilities, risk, and what to engineer next. * Set the scientific standard for what developability data we trust and report. WHAT WE'RE LOOKING FOR * Deep, hands-on experience assessing antibody developability — you know the assay panel cold and have used it to triage real antibody campaigns. * Applied industry experience. You've done this in an antibody discovery, engineering, or development setting where decisions and timelines were on the line — not primarily in an academic research project. This is the core requirement. * Strong grasp of what makes a biologic developable — the link between early biophysical signals and downstream manufacturability and stability. * Builder who moves fast. You want to stand up new capabilities and productize them, not run a fixed service. * Interdisciplinary. You collaborate naturally with automation engineers and with software/ML people, and you're excited about closing the loop between wet-lab data and predictive models. * Data fluency (scripting, working with structured datasets) is a strong plus. DETAILS * Location: Lausanne, Switzerland (on-site) * Type: Full time * Start date: ASAP Application deadline We are reviewing applicants on a rolling basis.