{"id":176510,"url":"https://alion.io/job/apheris-senior-ml-research-engineer","title":"Senior ML Research Engineer","company":{"id":63484,"name":"Apheris","domain":"apheris.com","url":"https://alion.io/company/apheris","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Personio","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_company_offices","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["DE"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":111000,"max_usd":246000,"period":"year","method":"global_role_seniority_cell","sample_n":2102},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false}],"status":"live","first_seen_at":"2026-08-21T11:17:09Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-10-17T16:17:41Z","board_verified":true,"closed_at":null,"days_open":57,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":57},"description":"About Apheris\nAt 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. \nAI Structural Biology (AISB) Network: Pharmaceutical companies collaborate in the field of co-folding, structure-based binding affinity predictions and antibody design.\nADMET Network: Pharmaceutical and biotech companies collaborate to improve small-molecule property prediction and expand into further drug modalities.\nAntibody 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.\nAbout the role\nWe 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.This 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.\nWhat you will do\nDevelop 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.\nBuild effective benchmarking and evaluation strategies for model evaluation and iterate and refine existing modelling approaches based on data-driven insights.\nDiagnose and resolve data quality and pipeline issues that affect model quality.\nStay up to date with a rapidly evolving research literature and identify best public approaches to aid in research and development.\nCollaborate with customers, partner-facing engineers, and external collaborators to support real-world drug design use cases.\nWhat we expect from you\nYou 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.\nYou have hands-on experience training, fine-tuning and extending deep learning models for molecular or protein structure modelling.\nYou are able to rigorously interrogate ML models, their training, and scientific benchmarks, and translate insights into impactful improvements.\nYou are an expert in Python and PyTorch, can produce reliable and clean code, and are comfortable with multi-GPU and distributed training.\nYou have deep familiarity with structural biology and protein-ligand data formats, quality metrics and tooling.\nYou proactively identify opportunities to contribute scientifically in order to impact the organizational goals.\nNice to have\nYou have experience in federated learning, privacy-preserving ML, or secure model training.\nYou have experience developing ML models for drug design in pharmaceutical or biotech environments.\nYou have published at top-tier ML or structural biology venues or have contributed to open-source projects in that space.\nWhat we offer you\nIndustry-competitive compensation, including early-stage virtual share options\nRemote-first working - 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