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.
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 to further drug modalities.
Antibody Developability Network:Pharma partners collaborate to federate historical and purpose-built antibody developability datasets for secure ML training, without data leaving each partner’s environment.
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About the role
We are building a new ML Research team within the broader AI Applications group at Apheris. AI Applications brings together multiple engineering teams responsible for delivering production-grade AI models for drug discovery, and this role will establish the dedicated research capability within that organisation. As the founding leader of the team, you will define its direction, build and mentor a high-performing group of researchers and engineers over time, and work directly on some of the most strategically important modelling questions across our structural biology and ADMET initiatives.
This is a player-coach role. You will be expected to set research direction, hire and mentor a team, and remain deeply involved in model development, experimentation, evaluation, and scientific problem-solving. The role is focused primarily on applied research rather than blue-sky research for its own sake: the emphasis is on taking strong ideas from the literature and adapting them to high-value biological and customer problems. Over time, as the team matures, there will be room to expand into more exploratory research directions.
In the first six months, a major focus will be the regularisation and generalisation of co-folding models. More broadly, the ML Research team will operate cross-functionally across Apheris initiatives, working across our small and large molecules networks to answer complex scientific questions and translate them into scalable modelling approaches with validated results.
You will also work with academic partners from leading labs, collaborate closely with interdisciplinary internal teams, and represent Apheris externally by presenting methods and findings to customers, partners, and at conferences.
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What you will do
Set up and lead the dedicated ML Research team within AI Applications, working alongside existing engineering teams andestablishingthe research mandate for the organisation.
Design, enhance, and train foundation models at scale for structural biology and co-folding, addressing core challenges in protein interaction modelling and drug discovery.
Leverage large-scale proprietary structural biology and biophysical datasets to develop improved data pipelines and model architectures that capture geometric and physical priors.
Translate advances in structural biology ML and adjacent literature into practical modelling approaches for real-world drug discovery problems.
Lead cross-functional delivery across AISB, ADMET, engineering, product, and privacy teams, ensuring research outputs integrate into production workflows.
Collaborate with academic partners on co-folding and structural biology research, contributing to publications and presenting findings at leading conferences.
Represent Apheris in customer discussions and scientific forums, and help solve high-impact modelling problems across multiple pharma partners.
Build and mentor a high-performing team of ML researchers and engineers over time.
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What we expect from youBy month 3:
Develop a deep understanding of the Apheris product, our current structural biology and ADMET initiatives, and the key scientific questions emerging from our networks. Define the initial research roadmap for AI Applications and begin hands-on work on the regularisation and generalisation of co-folding models.
By month 6:
Deliver initial results and customer-ready analyses for the first AI Applications workstreams, especially around co-folding model generalisation. Establish strong collaboration patterns across AISB, ADMET, engineering, privacy, and external academic partners. Clarify the capability and hiring plan for the team.
By month 12:
Lead a functioning ML Research team embedded within the broader AI Applications organisation, working across multiple initiatives at Apheris. Own a portfolio of applied research workstreams spanning co-folding and ADMET, and be recognised as a trusted technical authority in customer discussions, academic collaborations, and external scientific settings.
You should apply if:
You hold a postgraduate degree (PhD or MSc) in Computer Science, Machine Learning, Computational Biology, or a related field, and have 7+ years of relevant experience, including 3+ years in technical leadership.
You have strong experience applying machine learning to biological problems, particularly in structural biology (e.g. cofolding, protein modelling) or adjacent domains such as ADMET.
You have a proven publication track record in top-tier ML or computational biology venues (e.g. NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar).
You have hands-on experience with modern ML systems (Python, PyTorch) and have worked with or extended large-scale models (e.g. OpenFold, Boltz, or similar).
You are comfortable operating as a player-coach: setting technical direction, leading teams, and contributing directly to modelling and experimentation.
You are effective in cross-functional and customer-facing environments and can translate ambiguous scientific problems into clear technical approaches.
Bonus points if:
You have experience in early-stage biotech or in building ML systems or research functions from scratch.
You have experience training large models, including distributed training across GPU clusters or cloud platforms such as AWS, Azure, or Lambda.
You have strong ML Ops and machine learning infrastructure experience, particularly with Kubernetes-based workflows.
You have experience developing QSAR models with classical machine learning or deep learning methods.
You have experience writing Triton kernels or otherwise optimising model performance at the systems level.
You have experience in federated learning, privacy-preserving ML, or other multi-party training environments.
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What we offer you
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
