Senior ML Engineer

📋 Job Overview

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.

🏢 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.

🎯 The Role

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.

✅ Key Responsibilities

• Own the model pipelines end to end, from partner data to released, benchmarked model weights.
• Build validation that partners run alongside us, including schema and data contracts, validators that return actionable errors, and QC/profiling reports.
• Make federated runs reproducible and auditable with versioned configs, pinned data snapshots, and provenance for every released model.
• Build evaluation that survives scrutiny, including leakage-safe splitting, held-out benchmarks, and honest performance reporting.
• Harden research into product by turning prototypes and research code into tested, modular systems.
• Work across the boundary, translating scientific requirements into pipeline behavior with the science team.

📌 Required Qualifications

• 5+ years building ML systems in Python, with genuine software engineering discipline.
• Hands-on molecular ML or cheminformatics experience with RDKit, fingerprints and descriptors, or graph/transformer models.
• Experience building training and evaluation pipelines that other people run.
• Understanding of how molecular ML goes wrong and designing against common pitfalls.
• Ability to write validators and data contracts that hold up under partial visibility.
• Comfortable with PyTorch or an equivalent modern ML stack.
• Experience working well with scientists to translate ambiguous requirements into defined, testable pipeline behavior.

⭐ Desirable Experience

• Federated learning, privacy-preserving ML, or other multi-party training environments.
• ML Ops or ML infrastructure experience, particularly Kubernetes-based training, evaluation or deployment workflows.
• Production-grade model delivery in regulated, enterprise, pharmaceutical or biotech settings.
• Familiarity with public ADMET, toxicity and bioactivity data resources.
• Open-source contributions or a publication record in cheminformatics, molecular ML, or applied machine learning.

🎁 Benefits

• Industry-competitive compensation, including early-stage virtual share options
• Remote-first working - work where you work best
• Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget
• Generous holiday allowance
• Office days at our Berlin HQ or a different European location (3x per year)
• A high-calibre, execution-focused team with experience from leading organizations

🛂 Visa & Eligibility

No specific visa or eligibility information provided.