Technical Lead Structural Biology Networks
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Posted on:
4 hours ago
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1 Vacancy
Job Summary
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 industrys 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:Pharmaceuticalcompanies collaborate in the field of co-folding structure-based binding affinitypredictionsand antibody design.
- ADMET Network: Pharmaceutical and biotech companiescollaborate 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 partners environment.
About the role
We are looking for a technical lead to own delivery of our AI Structural Biology model programs.
This is a hands-on leadership role at the intersection of foundation models structural biology and federated learning. 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 challengemodelingdecisions 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.
This is a hands-on leadership role at the intersection of foundation models structural biology and federated learning. 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 challengemodelingdecisions 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.
What you will do
- Lead the teams building and deliveringfederated co-foldingmodels staying hands-on acrossmodeling architecture evaluation and engineering execution.
- Build and implementML applications in structural biology particularly around fine-tuning and extending foundational models likeOpenFold Boltz-2andESMFold.Own deliveryof theseagainst 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 decisionsand build on them to deliverresultspackages to external stakeholders.
- Surface risks blockers bugs timeline changes and technical trade-offs early with clear recommendations.
- Align consortium members onobjectives evaluation criteria data requirements timelines and delivery expectations.
- Work with product engineering research and leadership to ensure application requirements shape the model roadmap.
What we expect from you
You should apply if:
Bonus points if:
- You have a PhD MSc or equivalent experience in a relevant field plus 5 years applying ML to complex scientific or biological problems ideally in structural biology proteinmodeling co-folding or binding prediction.
- You have hands-on experience with modern ML systems in Python andPyTorch and have worked with or extended large-scale models such asOpenFold AlphaFold Boltz ESM or similar.
- You haveMLOpsor ML infrastructure experience particularly with Kubernetes-based training evaluation or deployment workflows.
- You can define success criteriavalidatemodel quality and ensure ML releases are robust enough for real-world use.
- You have led delivery of complex ML projects including setting technical direction managing risks and dependencies and driving teams toward high-quality releases.
- You are comfortable operating as a player-coach: mentoring engineers and ML scientists while contributing directly tomodeling experimentation or architecture when needed.
- You can work effectively with product research leadership customers and scientific stakeholders to turn ambiguous requirements into clear technical plans.
Bonus points if:
- You have experience with federated learning privacy-preserving ML distributed training or other multi-party training environments.
- You have experience with Go or other systems programming languages.
- You have worked on production-grade model delivery in regulated enterprise pharmaceutical biotech or other high-trust environments.
- You have a publication record in top-tier ML computational biology or structural biology venues such asNeurIPS ICML ICLR ISMB RECOMB or similar.
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
About Company
Build ML-powered products using data that spans organizational or geographical boundaries, while ensuring compliance with regulation.
