Scientist - Protein Binding Screening & Prediction

Are you ready for new challenges and new opportunities? Join our team in Seattle!
Current job opportunities are posted here as they become available.

The mission of the Allen Institute is to unlock the complexities of bioscience and advance our knowledge to improve human health. Using an open science, multi-scale, team-oriented approach, the Allen Institute focuses on accelerating foundational research, developing standards and models, and cultivating new ideas to make a broad, transformational impact on science.

Join our Artificial Intelligence-Powered Lab (AIPL), an initiative at the intersection of academic creativity and start-up style execution. Our mission is to apply machine learning to biological design. Join us as we build a series of interconnected design-test-loop “flywheels” that enable design of synthetic enhancers, protein binders, and more.

AIPL is looking for a scientist to join our protein binder flywheel team. The binder flywheel team is part of a larger effort that includes a total of three experimental flywheel teams and one machine learning team, and that will report into the Sea Hub administrative unit. The goal of the binder flywheel is to produce a model that can accurately predict the effect of amino acid substitutions on the binding of proteins to other proteins, small molecules, and nucleic acids.

To assist this flywheel team, you will need a strong background in protein library screening, protein binder design, or prediction of protein interactions with other molecules. You will work in the lab and collaborate with others, including machine learning colleagues, to optimize active learning processes in your flywheel. You will optimize your flywheel’s turn‑around‑time and per‑cycle model improvement, racing to deliver the best possible ML models for predicting the impact of mutations on protein binding.

At the Allen Institute, we believe that science is for everyone – and should be open to everyone. We are dedicated to combating biases and reducing barriers to STEM careers more broadly. We also believe that science is better when it includes different perspectives and voices. We strive to make the Allen Institute a place where everyone feels like they belong and are empowered to do their best work in a supportive environment.

We are an equal‑opportunity employer and strongly encourage people from all backgrounds to apply for our open positions.

• Assist in the building and running of a rapid, repeatable design–test–learn pipeline focused on protein binding
• Perform protein library screening and binder evaluation experiments to generate high‑quality, model‑ready datasets
• Partner with machine learning colleagues to help define prediction targets, curate/structure data, and implement active‑learning strategies that choose the next best experiments
• Assist in the optimization of flywheel performance by improving turnaround time, throughput, cost, and per‑cycle model lift; identify and remove experimental bottlenecks
• Present scientific results and complex concepts internally in talks and progress reports
• Present results to the external community at conferences and in publications

Note:

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. This description reflects management’s assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned.

PhD in biochemistry, molecular biology, or a related field; or equivalent combination of degree and experience

• 1 – 2 years relevant post‑doctoral or post‑grad biotech experience
• Experience designing or optimizing protein binders and characterizing binding (e.g., affinity/specificity) to proteins, small molecules, or nucleic acids
• Experience with protein library screening workflows (e.g., yeast/phage display + FACS/NGS or other high‑throughput binder discovery platforms)
• Experience building or operating data‑driven/active‑learning experimental loops and translating assay outputs into model‑ready datasets
• Experience with protein engineering/interaction prediction approaches…