Application Software Engineer

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 We build our lab automation ourselves. Instead of buying turnkey systems and running them through a vendor's GUI, we reverse-engineer instruments, control them with open-source Python tooling like PyLabRobot and PyHamilton, and build the software and hardware to turn a pile of instruments into work cells that run biology around the clock. We're looking for a Lab Automation Engineer to help design, build, and run these systems. This is a broad role: on any given week you might be writing a driver for a new instrument, getting a liquid handler and a robot arm to hand off cleanly, or debugging why an overnight run stalled. We care far more about range, ability, and drive than years on any one platform. WHAT YOU'LL DO * Control lab instruments — liquid handlers, robot arms, plate readers, incubators, analytical devices — programmatically in Python, ideally with PyLabRobot / PyHamilton, rather than locking workflows into proprietary software. * Build and integrate work cells: get multiple instruments working together as one orchestrated unit that runs unattended. * Bring new instruments online by reverse-engineering their control protocols and building drivers that expose them to the rest of the stack. * Write the software glue — drivers, integrations, small services — that connects hardware to our platform (mostly Python, with some TypeScript). * Partner with biologists to turn new assays into reliable automated workflows, then keep them running: diagnosing and fixing the mechanical, electrical, and software failures that come with 24/7 production. * Push throughput and reliability up continuously — better error recovery, smarter scheduling, sturdier hardware. WHAT WE'RE LOOKING FOR * Maker and hacker attitude. You like taking systems apart, figuring out how they work, and building the thing that makes them work better. You'd rather script an instrument than click through it. * Strong software engineering skills. You write real Python and can structure code others can maintain — drivers, integrations, services — not just throwaway scripts. You'll pick up TypeScript where needed. * Lab automation experience. Hands-on with PyHamilton, PyLabRobot, Opentrons, or similar tooling is a strong plus. Direct experience automating instruments and workflows matters more than the specific platform. * Self-starter and independent. You see what needs building and build it. You don't wait for a spec, and you don't need a vendor training course to get going. * 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. * Hardware comfort is a plus. Electronics, microcontrollers, 3D printing, CAD, or fabrication experience all help, but they're not required if your software and automation skills are strong. * Debugging instinct. When something fails mid-run, you reason across software, firmware, electronics, and mechanics to find the cause and fix it. Biology background not required — but you should be excited that the robots run real experiments. DETAILS * Location: Lausanne, Switzerland (on-site — you're building physical systems) * Type: Full time * Start date: ASAP We review applications on a rolling basis. 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. Adaptyv runs a physical lab through software. When our systems go down it isn't a page that fails to load — it's a liquid handler that stops mid-run, an instrument that loses its booking, or a customer's experiment that stalls with their protein already in a plate. Reliability here has physical-world consequences, and we need someone who owns it. You'll be responsible for the health of the entire stack that keeps LabOS and our customer-facing platform running: the APIs, edge functions, databases, processing pipelines, job queues, and the integrations that connect our software to millions of dollars of lab hardware. You'll build the observability, alerting, and automation that let a small team run a 24/7 automated lab without living in firefighting mode — and when something does break, you're the person who makes sure it gets caught early, fixed fast, and never happens the same way twice. In a given week, that might mean: * Building observability across the stack — metrics, logs, traces, and dashboards (Grafana) that make the state of the lab and the platform legible at a glance * Defining SLOs for the services that matter, instrumenting them, and setting up alerting that pages on real problems and stays quiet otherwise * Hardening our data and processing pipelines so messy physical-world data doesn't silently corrupt results or stall experiments * Owning incident response: triage, mitigation, and blameless postmortems that turn every outage into a permanent fix * Improving deploy safety and rollback across our services (Vercel, Supabase, Modal, edge functions) so shipping fast doesn't mean shipping fragile * Automating away toil — the manual recovery steps, the babysitting, the "just restart it" runbooks — so the lab runs itself as much as possible * Partnering with the software and lab-automation teams to make reliability a property of the system rather than an afterthought WHAT WE'RE LOOKING FOR * Strong systems and software engineering. You write production code (Python and/or TypeScript) and you're comfortable owning infrastructure, not just configuring it. * Real SRE / production ownership experience. You've run services that people depend on, carried a pager, and built the observability and automation that made on-call survivable. * Observability fluency. Metrics, logging, tracing, dashboards, alerting — you know how to make a complex distributed system legible, and you've used tools like Grafana / Prometheus / Loki (or equivalents) in anger. * Incident instinct. You stay calm when things break, find root cause fast, and you're allergic to the same incident happening twice. * Automation-first mindset. You'd rather spend a day automating a recurring 10-minute task than do it manually forever — and you build with coding agents like Claude Code as a default. * Pragmatic about reliability. You know the difference between what needs five nines and what doesn't, and you spend effort where it actually matters. * Bonus: where software meets the physical world. Hardware / lab / IoT, queues and pipelines, or cloud infra at scale — anything that has to keep running when there's something real on the other end. * Curious about biology. No background required, but you should find it genuinely interesting that the thing you're keeping alive is a lab where AI runs real experiments. Application deadline We are reviewing applicants on a rolling basis.