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.
We are looking for a Head of QC & Analytics to own the quality and data integrity of everything that comes out of our foundry. As we scale to tens of thousands of samples across hundreds of simultaneous experiments and conditions, the robustness of our analytical readouts and the reliability of our data pipelines become mission-critical.
This is a hands-on senior leadership role for someone who combines deep knowledge of protein biochemistry and analytical methods with the engineering instinct to automate, systematize, and continuously improve. You will define what "good data" means at Adaptyv, build the systems that enforce it at scale, and give our customers and internal teams full confidence in every data point we deliver.
Own end-to-end quality control for all protein assays run on the Adaptyv platform — binding (BLI/SPR), developability, and biophysical characterization workflows — across thousands of samples and diverse experimental conditions.
Define acceptance criteria, control standards, and QC metrics for each assay type and ensure they are systematically enforced in production.
Establish SOPs and quality checkpoints integrated directly into automated workcell workflows.
Lead investigation and resolution of out-of-specification results, assay failures, and systematic deviations — from root cause through corrective action.
Partner with assay development scientists to validate new methods before they enter production.
Our software team owns the data infrastructure, your role is to be their scientific counterpart. You define what needs to be measured, flagged, and reviewed; they build the systems that do it. This requires you to deeply understand the data, know where it breaks, and translate that into clear requirements.
Work closely with the software team to specify, review, and improve automated data processing pipelines for all instrument outputs, BLI, SPR, fluorescence readers, and other biophysical platforms.
Define what "good data" looks like for each assay type: expected signal ranges, control thresholds, failure modes, and edge cases the pipeline needs to handle.
Identify gaps in existing data review workflows and articulate precise requirements for how they should be improved, what to flag, what to auto-reject, what requires human review.
Own the scientific logic behind QC rules, anomaly detection, and data acceptance criteria, even when the implementation sits with engineering.
Review pipeline outputs critically: catch cases where automation gives a false pass or unnecessary fail, and feed those findings back as actionable engineering requirements.
Apply statistical reasoning to distinguish real assay drift or instrument variability from noise — and communicate those findings in a way the software team can act on.
Systematically identify manual bottlenecks in data review and QC sign-off and replace them with automated solutions
Collaborate with software and automation engineers to embed QC logic into our workcell control software and data infrastructure.
Drive the selection and implementation of laboratory informatics tools that support our scale.
B.Sc., M.Sc., or Ph.D. in Biochemistry, Analytical Chemistry, Biophysics, Bioengineering, or a closely related field.
6+ years of experience in a protein science, analytical development, or QC role — ideally in a high-throughput or CRO/foundry context.
Deep hands-on experience with protein binding assays, particularly BLI or SPR, including assay development, troubleshooting, and data interpretation.
Familiarity with developability and biophysical assays and their associated data quality challenges.
Strong programming skills in Python and/or R for data processing, QC automation, and pipeline development.
Proven experience managing large, multi-condition datasets, comfortable reasoning about quality across thousands of samples run in parallel.
Track record of building or significantly improving QC frameworks, SOPs, and data review processes in a production scientific environment.
Experience at a startup, CRO, or high-throughput foundry — you know what it means to build quality systems under speed and scale pressure.
Familiarity with instrument data formats and APIs from platforms such as Sartorius Octet, Cytiva Biacore, or equivalent biophysical instruments.
Background in statistical process control or equivalent quality methodologies applied to analytical data.
Exposure to AI/ML-driven protein design workflows and the data requirements of model training and benchmarking.
Experience with laboratory informatics platforms (LIMS, ELN) and data pipeline tooling.
Application deadline
We are reviewing applicants on a rolling basis.
