ML Engineer

📋 Job Overview

We are looking for an ML Engineer to join our large molecule ML team at Apheris, where you will build the models behind our antibody, co-folding and developability programs. This is a hands-on role at the intersection of foundation models, structural biology, protein engineering and federated learning.

🏢 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 will build, train and evaluate ML systems for antibody modeling, co-folding, developability prediction and biologics discovery, working on proprietary pharma data across our federated networks. You will own substantial parts of our model programs end to end, taking research-led or open-source prototypes and turning them into models that can be evaluated, released and used in real drug discovery workflows.

✅ Key Responsibilities

• Build, fine-tune and extend large biomolecular models such as OpenFold, Boltz-2 and ESM for antibody modeling, co-folding, binder prediction and developability.
• Turn research code and prototypes into reliable components that run in our federated training and evaluation pipelines.
• Design evaluations and benchmarks, and deliver results packages for consortium partners.
• Own workstreams through to release against agreed milestones, raising risks and trade-offs early.
• Work with product, engineering, research and consortium members to make sure the model work meets real application needs.

📌 Required Qualifications

• An MSc, PhD or equivalent experience in machine learning, computational biology, bioinformatics, physics or a related field
• Strong Python and PyTorch, and hands-on experience training or fine-tuning deep learning models on biomolecular data.
• Hands-on experience with co-folding models or protein language models, such as OpenFold, AlphaFold, Boltz, ESM or similar, beyond just running inference.
• Good evaluation habits and solid engineering practice: fair benchmarks, reproducible experiments, and code other people can build on.

⭐ Desirable Experience

• Experience with Kubernetes-based training, evaluation or deployment, or other MLOps and ML infrastructure tooling.
• Experience with federated learning, privacy-preserving ML, or distributed and multi-GPU training.
• Experience in pharma, biotech or other regulated or high-trust environments.
• Publications in ML, computational biology or structural biology venues such as NeurIPS, ICML, ICLR or similar.

🎁 Benefits

Industry-competitive compensation, including early-stage virtual share options. Remote-first working with a great suite of benefits, including a wellbeing budget, mental health benefits, a work-from-home budget, a co-working stipend and a learning and development budget. Regular team lunches and social events, generous holiday allowance, quarterly All Hands meet-up at our Berlin HQ or a different European location. A fun, diverse team of mission-driven individuals with a drive to see AI and ML used for good. Plenty of room to grow personally and professionally and shape your own role.

🛂 Visa & Eligibility

No specific visa or eligibility information provided.