ML Application Engineer (Japanese or Korean-speaking)

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.

Neural Concept

About the Role The mission of Neural Concept's Application Engineering team is to help industry leaders redefine how they design products — by bridging cutting-edge AI technology with real-world engineering challenges. We work hand-in-hand with engineering, design, and executive teams across automotive, aerospace, and energy to understand their strategic needs and deliver solutions that create measurable impact. As part of this team, you will sit at the intersection of business and technology — translating complex customer challenges into high-value AI-powered solutions, and ensuring those solutions actually land and stick within our customers' organizations. This is a role for someone who thrives equally in a boardroom conversation with a VP and in a deep-dive technical session with engineers. If you're excited about shaping how industry leaders adopt AI and about being a trusted advisor to some of the world's most sophisticated engineering teams, this is the role for you. What you will do * Understand and shape customer strategy: Engage with senior stakeholders (VP, C-level) to understand their business challenges, articulate the value of AI-assisted design, and co-design high-level solution roadmaps. * Design and deliver solutions: Translate business requirements into concrete technical approaches using NC's platform, working closely with customer engineering teams and internal developers. * Drive adoption: Guide customers through AI adoption — from proof-of-concept to full integration — ensuring teams are equipped and confident to use new workflows. * Be the bridge: Act as the link between customer needs and NC's product evolution, feeding insights back to our development team to continuously improve the platform. Who you are * You are a strong communicator and trusted advisor — you can engage confidently with senior stakeholders (VP, C-level), build relationships quickly, and drive alignment around complex, ambiguous problems; * You have experience designing solutions end-to-end: from understanding a business need to translating it into a concrete technical approach; * You are comfortable working in a customer-facing environment, navigating both executive conversations and deep technical sessions with engineering teams; * You hold a Master's or PhD degree in Engineering, Applied Mathematics, Physics, or a related field — or have equivalent experience in an industrial or consultancy setting; * You have a solid understanding of engineering simulation concepts (CAD/CAE, CFD, FEM, or similar) — whether from an academic or industry background; * You have working knowledge of machine learning and how it applies to engineering problems; hands-on experience with Python and ML/deep learning frameworks is a strong plus; * You are intellectually curious and eager to tackle real-world engineering challenges through AI and automation; * You are fluent in English; French is a plus; What You Get * Work with a world-class technology team – our engineers are top-notch, and we always aim for excellence. * Benefit from a competitive salary and rewarding opportunities as we continue to scale. * Thrive in a collaborative, multicultural environment where your work is visible and recognized. * Develop professionally alongside talented colleagues who share knowledge freely and support one another. * Make a global impact by helping customers shift to AI-assisted design, making innovation faster, smarter, and more sustainable. * Balance life and work with a hybrid model and flexible hours—we care about results, not rigid schedules. WE'RE PROUD TO BE AN EQUAL OPPORTUNITY EMPLOYER, AND WE'RE COMMITTED TO BUILDING A DIVERSE AND INCLUSIVE ENVIRONMENT WHERE YOU CAN THRIVE.

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.