---
title: 'Scientist, Binding Kinetics at Adaptyv'
canonical: 'https://feeny.ai/job/scientist-binding-kinetics-adaptyv-lausanne-48nwzje20zap'
type: 'job'
last_seen: '2026-10-24'
---

# Scientist, Binding Kinetics at Adaptyv

- **Company:** Adaptyv
- **Location:** Lausanne, Switzerland
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-19
- **Last confirmed live:** 2026-10-24
- **Apply:** https://jobs.ashbyhq.com/adaptyv/88451659-b23b-4f27-a082-2e5635d8126a

## Job description

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

Our lab produces binding data faster than any one person can look at it. Runs come off the Gators and the Carterra, go through our processing pipeline, and land in a review queue. Somebody with real judgment then has to decide: is this curve real? Is that a fit artifact or a genuine two-state binding event? Does this replicate get delivered, or does the sample get re-run? That person is you. The second half of the job is talking to the customer about it. When a design team gets a plate back and three of their best predictions look like non-binders, they want to talk to someone who can explain why: reference subtraction, avidity, an off-rate at the edge of what the instrument resolves, or simply a bad protein. You'll be that person, on the call, sharing your screen, walking through sensorgrams. This is a role for a scientist who is very good at kinetics and also likes people. If you like one but not the other, you'll be unhappy here.

## What You'll Do

- Review the binding data coming out of the lab. Every SPR and BLI run passes through our processing pipeline and then to human review, and you're the expert eye on the output. You decide what gets delivered, what gets flagged, and what gets re-run.
- Judge the curves, not just the numbers. A clean-looking Kd on a bad fit is worse than no number at all. You know the difference between a real off-rate and a drifting baseline, and you can say why.
- Review data from our other assays too: thermostability, expression, developability, enzyme activity. Binding is the core of the role but it isn't all of it.
- Talk to customers about their results. Deliverable walkthroughs, follow-up questions, and the awkward conversation when the data doesn't say what they hoped. You explain what we measured, how confident we are, and what to do next.
- Turn review into experimental design. When something needs a re-run, you decide what changes: orientation, regeneration, concentration series, or a switch from BLI to SPR for the sensitivity.
- Analyze at the batch level, not just the sample level. With hundreds of results per experiment, you should be writing a script to find the pattern rather than clicking through a queue. Systematic drift, a bad reference channel, or a target lot behaving differently than the last one shows up in aggregate long before it shows up in any single curve.
- Feed what you learn back into the pipeline. When you catch a case where automation passed something it shouldn't have, or failed something it shouldn't have, that becomes a concrete requirement for the software team.
- Work closely with production (what actually happened in the lab), QC (systematic quality), and customer success (the relationship around the science).

## What We're Looking For

- MSc / PhD in biochemistry, biophysics, or a relevant field, plus 3+ years working hands-on with binding kinetics.
- Deep SPR and BLI experience. Biacore, Carterra, Octet, Gator: the platform matters less than the fact that you've run these assays, troubleshot them, and interpreted a lot of data from them. You've seen enough sensorgrams to know immediately when one is lying to you.
- Real fitting judgment. 1:1 vs. bivalent, when steady-state is appropriate and when it isn't, how mass transport and avidity distort a result. You can defend a Kd, or refuse to report one.
- Strong data analysis skills. Python or R, enough to pull a few hundred results out of a database, fit them, plot them, and find the outliers. This role generates too much data to review by hand.
- You use AI tools seriously. We run on Claude Code and similar tooling internally, and the people who get the most done here are the ones who reach for it by default: writing the analysis script, querying the database, building the one-off dashboard. You don't need to have done this before, but you need to want to. If your instinct is that real scientists do it by hand, this isn't the place.
- A self-starter who works independently. Nobody is going to hand you a prioritized queue every morning. You'll figure out what matters most, go do it, and tell us what you found.
- A fast learner. Our platform, our assays, and our software change constantly. You'll be handed unfamiliar tools and expected to be productive with them quickly.
- Startup speed, not academic pace. Experiments ship to paying customers on deadlines. Getting a good answer this week beats a perfect one next quarter, and we'd rather flag an ambiguous result than sit on it.
- Comfortable in front of customers. Many are PhD-level scientists at pharma companies and AI labs, and they will push back. You should enjoy that conversation rather than dread it.
- Able to explain hard things simply. Some customers are biophysicists. Some are ML researchers who have never touched a pipette. You adjust without condescending to either.
- Honest about uncertainty. When the data is ambiguous, you say so, to us and to the customer. We would much rather flag a weak result than ship a confident wrong one.
- Familiarity with other biophysical or functional assays (DSF, HPLC, plate-based activity assays) is a plus.

Details

- Location: Lausanne, Switzerland (on-site)
- Type: Full time
- Start date: ASAP

Application deadline We are reviewing applicants on a rolling basis.

## About Adaptyv

## Company Overview
- **One-liner**: Adaptyv is a cloud lab that enables protein designers to validate and test novel protein designs using fully automated labs, delivering clean, AI-ready experimental data in under three weeks.
- **Entity Type**: Private (Seed stage — raised $10.9M total; last round: $8.0M Seed led by ACE Ventures in October 2025)
- **Headquarters**: Lausanne, Switzerland
- **Founded**: 2021
- **Founders**: Daniel Nakhaee-Zadeh Gutierrez (Co-Founder) and Julian Englert (Co-Founder & CEO)

## Core Business
- **Primary industry**: Biotechnology research / Protein engineering / Synthetic biology lab services
- **Target customers**: B2B — AI-first protein engineering teams, computational biologists, biotech/pharma R&D groups (both startups and established enterprises)
- **Mission statement**: “To make proteins easier to engineer” by building technologies that allow protein engineers to design and test new proteins for medicines, industrial enzymes, and functional materials [adaptyvbio.com](https://www.adaptyvbio.com/).

## Products & Services
- **Adaptyv Cloud Lab** (Primary offering): A fully automated protein validation lab. Clients submit protein sequences digitally; Adaptyv performs synthesis, expression, purification, and a wide range of assays, returning raw and labelled data in under 3 weeks. Priced at **$149 per protein** (includes 2 replicates). Data is accessible via a modern experiment management platform, eliminating email-based quoting and Excel sheets [adaptyvbio.com](https://www.adaptyvbio.com/).
- **Adaptyv API**: Programmatic access to the lab, allowing seamless integration into users’ design–build–test workflows. Supports high-throughput and automated submission [adaptyvbio.com](https://www.adaptyvbio.com/).
- **Experiment Management Platform**: Web-based dashboard to order assays, monitor progress, analyze results, and track historical performance of protein design rounds [adaptyvbio.com](https://www.adaptyvbio.com/).

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metrics**:
  - **Revenue**: $1.0M (estimated annual revenue per LinkedIn) [linkedin.com](https://ch.linkedin.com/company/adaptyvbio)
  - **Total Funding**: $10,939,391 across four rounds (Pre-Seed via Y Combinator, Pre-Seed led by Founderful, Debt from Foundation for Innovation and Technology, and Seed from ACE Ventures) [linkedin.com](https://ch.linkedin.com/company/adaptyvbio)
- **Notable Investors**: Y Combinator (Summer 2021 batch), Founderful, ACE Ventures (lead Seed round), Foundation for Innovation and Technology (FIT) [linkedin.com](https://ch.linkedin.com/company/adaptyvbio)
- **Growth Signals**:
  - Team size grew from ~25 (Y Combinator profile, 2025) to 31 employees (LinkedIn, 2026) — a **65% year-over-year increase** [ycombinator.com](https://www.ycombinator.com/companies/adaptyv) [linkedin.com](https://ch.linkedin.com/company/adaptyvbio)
  - 30 open positions listed on careers page (April 2026) [adaptyvbio.com/careers](https://www.adaptyvbio.com/careers)
  - Operates in 7 countries, with the majority of the team in Switzerland [linkedin.com](https://ch.linkedin.com/company/adaptyvbio)

## Competitive Advantages
- **Speed**: Fastest protein validation lab — “go from sequence design to experimental data in under 3 weeks” [adaptyvbio.com](https://www.adaptyvbio.com/)
- **Data quality**: Automated, standardized lab processes produce consistent, labelled data that is directly useful for training generative AI models (billed as “lab-in-the-loop protein design”)
- **Transparent per‑protein pricing**: No hidden costs, no RFQ cycles — $149/protein simplifies budgeting for teams of all sizes
- **Purpose‑built for AI‑first workflows**: Seamless API and platform integration enables iterative design cycles, reducing time between computational predictions and wet-lab validation [adaptyvbio.com](https://www.adaptyvbio.com/)

## Strategic Focus
- **Current priorities**: Scaling lab automation capacity to handle increasing demand; expanding the range of assays offered (biophysical, functional, cell-based); deepening integrations with AI protein design tools; growing the team across biology, software, and operations.
- **Direction**: To become the de facto “cloud lab” layer for the protein design stack, powering the world’s best AI-driven protein engineering teams [adaptyvbio.com](https://www.adaptyvbio.com/) [linkedin.com](https://ch.linkedin.com/company/adaptyvbio)

## Why Work Here
- **Culture & Values**: Emphasizes first‑principles thinking, data‑driven decision‑making, an action‑oriented mindset, and strong work ethic. The team is composed of talented engineers from molecular biology, lab automation, software engineering, and machine learning [adaptyvbio.com/careers](https://www.adaptyvbio.com/careers)
- **Location & Flexibility**: Headquarters in Lausanne, Switzerland. Most roles offer **hybrid** or **remote** arrangements (within Switzerland/Europe), with some roles listed as Lausanne/Hybrid/Remote [adaptyvbio.com/careers](https://www.adaptyvbio.com/careers)
- **Perks & Environment**: A chance to shape the infrastructure of protein engineering — building revolutionary technologies in synthetic biology and lab automation. The team has strong academic and startup roots (EPFL, Harvard, YC). Open roles include Software Engineers, AI/ML Engineers, Lab Automation Engineers, Scientists (Protein Expression, Antibody Developability, Cell-Based Assays), and Operations roles.
- **Open Positions**: 30 roles across software, biology, lab automation, and business development as of April 2026 [adaptyvbio.com/careers](https://www.adaptyvbio.com/careers)

## Sources
1. [adaptyvbio.com](https://www.adaptyvbio.com/)
2. [adaptyvbio.com/careers](https://www.adaptyvbio.com/careers)
3. [ycombinator.com](https://www.ycombinator.com/companies/adaptyv)
4. [linkedin.com](https://ch.linkedin.com/company/adaptyvbio)
5. [adaptyvbio.com/team](https://www.adaptyvbio.com/team)

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