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Applied ML Scientist

We’re hiring an Applied ML Scientist to join our fast-paced platform therapeutics startup!

We need scientists and engineers who own their work, adapt quickly without losing precision, and stay laser-focused on what truly matters. We are looking for exceptionally skilled pioneers with a translational mindset - people who don’t just explore problems but drive solutions. We do things thoroughly, well, and at speed, because that’s what it takes to make an impact. If you thrive in a high-impact, no-nonsense environment* where we innovate together to drive results, we want you on our team!

* much like the mitochondria in a cell!

Tell us a bit more about yourself

Are you legally authorized to work in the United States?

Please confirm that you meet the minimum requirements for this role.

Do you hold a degree in physics, applied mathematics, engineering, or a closely related quantitative discipline?

Do you have at least 2 years of recent, full-time experience applying modern ML frameworks to image data?

Please note: Your responses to these questions are used only to assess whether you meet the minimum requirements for this role. If your answers and resume do not clearly demonstrate that you meet these requirements, we unfortunately won’t be able to proceed with your application or provide individual feedback.

Startups are fast, candid, occasionally chaotic, and do not entail cushions. Priorities can flip in a day; we ship weekly; everyone - from founders to scientists - owns outcomes end-to-end. Are you excited to work in this environment?

Please upload your CV or resume

Accepts .pdf files

Size limit: 10 MB

You’re given data from a 96-well high-content imaging screen capturing cell morphology across conditions. You'd want to nominate 10 conditions for wet-lab validation. Your end-to-end approach consists of: preprocessing and QC, representation learning choices, the multi-modal integration method, data-splits/controls to prevent leakage, calibration/uncertainty estimation, graph-based priors, decision metrics for ranking, and a structure of the first validation loop to close the learning cycle. What is most difficult, and why?