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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. About the role We are looking for an ML Engineer to join our large molecule ML team and 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. 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. That means taking research-led or open-source prototypes and turning them into models that can be evaluated, released and used in real drug discovery workflows. About you You're an ML engineer who works close to the science. You've trained and evaluated models on biological data, and you can take a paper or an open-source model and make it work on a new problem. You care about whether a model actually holds up, not just whether the numbers look good, and you want your work to end up in real use by pharma R&D teams. What you will do • 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. What we expect from you • 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. Nice to have • 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. What we offer you • 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

About Apheris Apheris hosts the industry's largest federated data networks for drug discovery AI, spanning co-folding, ADMET, and antibody developability. Leading pharma companies train higher-performing models on this data without ever exposing it, and we deliver those models through Apheris Foundry, our drug discovery platform. About the role We're building out the team that turns our networks into models pharma teams actually use. You'll scope and stand up new networks, run execution end to end with partners like the world's top pharma companies, and get Apheris Foundry embedded into their live drug discovery programs, not just evaluated, actually used. This is a high-ownership, high-visibility seat. You'll be the person top pharma scientists call when something needs to move, and the person who turns a network from concept to models running in production. Our networks are scaling fast and the work is only getting bigger from here. About you You know molecular ML and drug discovery workflows well enough to hold your own with computational chemists, DMPK scientists, and structural biologists. You get concrete fast, even under pressure, and you'd rather show a plan than describe one. You've run complex, multi-party programs before and you know the difference between managing a process and actually owning an outcome. We're looking for someone with 3+ years in scientific or technical program management, alliance management, or customer-facing delivery of scientific software, who wants real ownership over how our networks and our platform land with some of the biggest names in pharma. What we offer you Competitive compensation with early-stage virtual share options, a remote-first setup with real flexibility on where you work, and genuine investment in your wellbeing: mental health support, a work-from-home and co-working budget, a learning budget, and generous holiday. We bring the team together three times a year, in Berlin and elsewhere in Europe.