About Apheris

At Apheris, we are building the future of how AI is applied in pharmaceutical R&D. We enable leading pharmaceutical teams to discover and develop drugs faster. We host the industry’s largest federated data networks for drug discovery AI, spanning co-folding, ADMET, and antibody developability. Across these networks, models are trained on proprietary industry datasets to achieve higher performance and broader applicability while keeping data control and IP protected. We deliver these superior models through drug discovery applications that enable teams to run them at scale, further customize them, and integrate them into existing R&D workflows.

About the role

We're looking for a senior ML engineer to own the training and evaluation pipelines behind our ADMET and toxicity networks - the machinery that turns partner data into trained, benchmarked, released models, run after run. This is a hands-on engineering role at the point where molecular ML meets federation. Your code runs inside partner environments, on data you cannot see, alongside scientists at some of the largest pharma companies in the world. You'll work closely with the scientific lead for toxicity: they own what we model and why, you own how it gets built, validated, evaluated and shipped - reliably enough that a partner will stake a drug program on the result. The ADMET network is live and expanding into toxicity. You'll be building the pipelines that expansion runs on.

What you will do

Own the model pipelines end to end. Take partner data from landing to released, benchmarked model weights - data preparation, training, evaluation, release - for ADMET and toxicity endpoints.
Build the validation that partners run alongside us. Schema and data contracts, validators that return actionable errors, and QC/profiling reports that a partner can act on without us seeing their raw data.
Make federated runs reproducible and auditable. Versioned configs, pinned data snapshots, provenance for every released model - so we can say exactly what produced a given set of weights.
Build evaluation that survives scrutiny. Leakage-safe splitting, held-out benchmarks and honest performance reporting, so the numbers we put in front of partners hold up.