Technical Lead – Large Molecule AI Systems

Apheris

6d•Remote

About The Position

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 a technical lead to own delivery of our large molecule AI model programs. This is a hands-on leadership role at the intersection of foundation models, structural biology, protein engineering, and federated learning. You will lead teams building and operationalizing large-scale ML systems for antibody modeling, co-folding, developability prediction, and biologics discovery. You will turn ambitious scientific goals into reliable model systems that can be evaluated, released, and used in real drug discovery workflows. You will set technical direction, drive execution, challenge modeling decisions, and turn ambiguity into executable plans, while managing risks and dependencies, mentoring senior engineers and ML scientists, and getting into technical depth when needed. We are looking for someone who has led demanding ML delivery before and knows how to move from research-led or open-source prototypes to robust model systems.

Requirements

PhD, MSc, or equivalent experience in a relevant field, plus 5+ years applying ML to complex scientific or biological problems, ideally in structural biology, antibody engineering, biologics discovery, developability prediction, binder prediction or protein design.
Hands-on experience with modern ML systems in Python and PyTorch, and have worked with or extended large-scale models such as OpenFold, AlphaFold, Boltz, ESM, or similar.
MLOps or ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows.
Ability to define success criteria, validate model quality, and ensure ML releases are robust enough for real-world use.
Experience leading delivery of complex ML projects, including setting technical direction, managing risks and dependencies, and driving teams toward high-quality releases.
Comfortable operating as a player-coach: mentoring engineers and ML scientists while contributing directly to modeling, experimentation, or architecture when needed.
Ability to work effectively with product, research, leadership, customers, and scientific stakeholders to turn ambiguous requirements into clear technical plans.

Nice To Haves

Experience with federated learning, privacy-preserving ML, distributed training, or other multi-party training environments.
Experience working on production-grade model delivery in regulated, enterprise, pharmaceutical, biotech, or other high-trust environments.
A publication record in top-tier ML, computational biology, or structural biology venues such as NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar.

Responsibilities

Lead teams building and delivering federated large molecule AI systems, staying hands-on across antibody modeling, co-folding, binder prediction, and developability.
Build and implement ML applications large biomolecular foundation models such as OpenFold, Boltz-2 and ESM.
Own delivery of these against committed milestones and ensure high-quality model releases ship on time.
Translate ambiguous scientific and technical goals into clear plans, priorities, workstreams, and decisions.
Guide evaluation decisions and build on them to deliver results packages to external stakeholders.
Surface risks, blockers, bugs, timeline changes, and technical trade-offs early, with clear recommendations.
Align consortium members on objectives, evaluation criteria, data requirements, timelines, and delivery expectations.
Work with product, engineering, research, and leadership to ensure application requirements shape the model roadmap.