Forward-Deployed ML Engineer

📋 Job Overview

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.

🏢 About Apheris

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.

AI Structural Biology (AISB) Network: Pharmaceutical companies collaborate in the field of co-folding, structure-based binding affinity predictions and antibody design.

ADMET Network: Pharmaceutical and biotech companies collaborate to improve small-molecule property prediction and expand into further drug modalities.

Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability data sets for secure ML training, without data leaving each partner’s environment.

🎯 The Role

At Apheris, we power federated data network in life sciences to address the data bottleneck in training highly performant ML models. Publicly available, molecular datasets are insufficient to train high-quality ML models that meet industry requirements. Our product addresses this by hosting networks where biopharma organizations collaboratively train higher quality models on their combined data. The Apheris product is a set of drug discovery applications - enriched with the proprietary data of network participants. Our federated computing infrastructure with built-in governance and privacy controls ensure that the data IP and ownership always stays with the data custodians.

As we are doubling down on structural biology use cases as a focus area within our drug discovery work, we are looking for a Senior ML Engineer to drive the technical execution for our structural biology models. This is a hands-on, high-impact role focused on advancing the state of the art in applying foundational models to structural biology problems. You’ll work closely with our leadership team and will serve as the technical authority on ML modelling, architecture, and experimentation in this domain.

✅ Key Responsibilities

• Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold.
• Design and implement model extensions for specific tasks such as protein complex and binding affinity prediction, including data distillation, benchmarking, and evaluation pipelines.
• Work with our customers and academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data.
• Carry out case-studies associated with the above, providing scientific and technical expertise to our customers. You will be involved in the full project pipeline, from scoping through to results delivery and dissemination.
• Design, build, and maintain scalable machine learning models and the pipelines needed for training, inference, and deployment in production.
• Collaborate cross-functionally to ensure models address real-world drug discovery needs.
• Contribute to publications or open-source contributions where relevant.

📌 Required Qualifications

• You have deep experience building and training contemporary models in production, at scale (e.g. AlphaFold, OpenFold, Boltz) and are familiar with modern MLOps tooling.
• You have experience applying ML to real-world protein structure or drug discovery problems.
• You are comfortable working in a fast-paced startup environment and enjoy on customer-driven projects.
• You understand the technical challenges of structural biology and can design scalable data preprocessing, training, and evaluation workflows.

⭐ Desirable Experience

• You have experience in federated learning, privacy-preserving ML, or privacy-preserving model training.
• You’ve published in ML or biology journals/conferences (e.g., NeurIPS, ICML, Nature Methods, Bioinformatics).

🎁 Benefits

• Industry-competitive compensation, incl. early-stage virtual share options
• Remote-first working – work where you work best, whether from home or a co-working space near you
• 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