Systems Engineer CH

Chief Engineer

Harmattan AI

ABOUT US Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces. Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected. About the Role As a Chief Engineer, you will own the architecture, system definition and implementation of one of our robotic systems — from concept to field deployment. You will work hand-in-hand with project managers and technical leads to translate mission needs into system-level designs, coordinate multidisciplinary engineering teams, and ensure overall system performance, robustness, and reliability. Responsibilities * System Architecture: Transform system requirements into architectures solutions. * Technical Coordination: Lead cross-functional design across mechanical, electrical, software, GNC, and ML teams. * Trade-Off & Design Analysis: Conduct feasibility studies, trade-off analyses, and system-level simulations to guide technical choices. * Integration & Validation: Oversee system integration, prototyping, and validation, ensuring performance targets are achieved. * System Reviews: Drive design and architecture reviews, maintaining consistency between requirements, design, and testing. * Continuous Improvement: Implement processes to enhance requirements traceability, system documentation, and validation workflows. Candidate Requirements * 10+ years of experience in system engineering within robotics, aerospace, or defense sectors. * MSc in Engineering (any field); PhD is a plus. * Proven experience leading full system lifecycles (concept → prototype → validation). * Strong background in system architecture, requirements engineering, and trade-off analysis. * Broad engineering culture with understanding of mechanical, electrical, and embedded systems. * Experience coordinating multi-disciplinary teams and managing technical interfaces. * Rigorous, structured, and mission-driven mindset with strong analytical skills. * Excellent communication and documentation abilities. * Bonus: Experience with UAS or defense systems. We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.

Software Engineer, Lab Automation

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.

AI 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 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. 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. 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 won't be training protein language models or publishing papers. 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. 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. 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. * 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. * 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. The AI touches everything. * Ships fast, owns the result. You prototype in a day, get feedback, iterate. And you're responsible for everything your agents produce — shipping fast does not mean dumping slop on the rest of the team. 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.