Adaptyv is building an automated lab thats let 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.
You're a scientist on staff who works shoulder-to-shoulder with customers to help them design better proteins and get more out of every experiment. You sit between their design models and our lab: helping them scope a campaign, pick the right design approach for the problem, make sense of the data that comes back, and decide what to try next.
This is a hybrid role for a scientist who genuinely likes solving problems with people. One week you might be helping an AI lab choose between de novo design and a fine-tuned model for a hard target; the next, digging into a customer's binding data to work out why a design class underperformed and what to change. You'll turn one-off experiments into iterative design-build-test campaigns — and, over time, into deep, lasting partnerships.
Partner with customers to scope protein design campaigns: define the target, the success criteria, and the experimental plan to get there.
Advise on design strategy — which models and methods (de novo design, diffusion models, protein language models, structure prediction, physics-based tools) fit which problems, and where their limits are.
Help design the right experiments to test a hypothesis: what to express, what controls to include, which assays will actually answer the question.
Analyze experimental results with customers — binding, expression, stability — and translate data into the next round of designs. Deep experience with biophysical characterization techniques like SPR, BLI, nanoDSF etc is a must.
Close the loop: turn each campaign's results into sharper designs and a tighter iteration cycle.
Build trusted relationships that grow from a first project into long-term scientific partnerships.
Feed what you learn back to our product, science, and platform teams so the whole experience gets better.
Protein science background. A degree and/or hands-on experience in protein design, computational biology, biochemistry, structural biology, or a closely related field.
Fluent in modern protein design. You understand today's design and prediction models well enough to advise on which to use when — and you keep up as the field moves.
Data-driven. You're comfortable analyzing experimental data (Python, notebooks, basic stats) and drawing clear conclusions from messy, real-world results.
Loves working with people. You're a strong communicator who's energized by solving hard problems alongside customers, not just at a desk.
Scientific problem-solver. When a campaign isn't working, you form hypotheses, design experiments to test them, and iterate — you don't guess.
Autonomous and curious. You take ownership of a customer's success and go deep on whatever the science demands.
Experience working with external partners or customers is a plus, but the core is being a great scientist who likes solving problems in the open.
Application deadline
We are reviewing applicants on a rolling basis.
