Senior ML Research Engineer

h3About Apheris /h3 pAt Apheris, we are building the future of how AI is applied in pharmaceutical RD. 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. /p h3About Apheris /h3 pAt Apheris, we are building the future of how AI is applied in pharmaceutical RD. 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. /p pAcross these networks, models are trained on proprietary industry datasets to achieve higher performance and broader applicability while keeping data control and IP protected. /p pWe deliver these superior models through drug discovery applications that enable teams to run them at scale, further customize them, and integrate them into existing RD workflows. /p ul liAI Structural Biology (AISB) Network: Pharmaceutical companies collaborate in the field of co-folding, structure-based binding affinity predictions and antibody design. /li liADMET Network: Pharmaceutical and biotech companies collaborate to improve small-molecule property prediction and expand into further drug modalities. /li liAntibody 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. /li /ul h3About The Role /h3 pWe are looking for a Senior Machine Learning Research Engineer to help drive the research and development of machine learning models for molecular and structural biology. /p pThis is a hands‑on role at the intersection of foundation models, structural biology, and federated learning. You'll execute research projects, turning ambitious scientific goals into frontier ML models that can be evaluated, released, and used in real drug‑discovery workflows. /p h3What you will do /h3 ul liDevelop and improve ML models in molecular and structural biology, such as co‑folding and binding affinity models, for drug design applications and workflows, driving them from ideation through prototyping iterations to robust tooling. /li liBuild effective benchmarking and evaluation strategies for model evaluation and iterate and refine existing modelling approaches based on data‑driven insights. /li liDiagnose and resolve data quality and pipeline issues that affect model quality. /li liStay up to date with a rapidly evolving research literature and identify best public approaches to aid in research and development. /li liCollaborate with customers, partner‑facing engineers, and external collaborators to support real‑world drug design use cases. /li /ul h3What we expect from you /h3 ul liYou have a PhD or MSc in machine learning, computational biology, computational chemistry, bioinformatics, physics, or a related field, with at least 2 years of professional experience applying ML to scientific problems. /li liYou have hands‑on experience training, fine‑tuning and extending deep learning models for molecular or protein structure modelling. /li liYou are able to rigorously interrogate ML models, their training, and scientific benchmarks, and translate insights into impactful improvements. /li liYou are an expert in Python and PyTorch, can produce reliable and clean code, and are comfortable with multi‑GPU and distributed training. /li liYou have deep familiarity with structural biology and protein–ligand data formats, quality metrics and tooling. /li liYou proactively identify opportunities to contribute scientifically in order to impact the organizational goals. /li /ul h3Nice to have /h3 ul liYou have experience in federated learning, privacy‑preserving ML, or secure model training. /li liYou have experience developing ML models for drug design in pharmaceutical or biotech environments. /li liYou have published at top‑tier ML or structural biology venues or have contributed to open‑source projects in that space. /li /ul h3What

we offer
you /h3 ul liIndustry‑competitive compensation, including early‑stage virtual share options /li liRemote‑first working – work where you work best, whether from home or a co‑working space near you /li liGreat 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 /li liGenerous holiday allowance /li liOffice Days at our Berlin HQ or a different European location (3x a year) /li liA fun, diverse team of mission‑driven individuals with experience across leading organizations and a drive to see AI and ML used for good /li /ul