Technical Lead - Structural Biology Networks
New
UKFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonKubernetesMachine LearningPyTorchMLOps
Requirements
- PhD, MSc, or equivalent experience in Machine Learning, Computational Biology, Computer Science, or a related field, with 5+ years of applied ML experience in complex scientific or biological domains.
- Strong hands-on expertise in structural biology ML, including protein modeling, co-folding, or binding prediction.
- Proven experience working with modern ML frameworks such as Python and PyTorch, and extending large-scale models like OpenFold, AlphaFold, Boltz, or ESM.
- Experience with MLOps or ML infrastructure, including Kubernetes-based training, evaluation, or deployment pipelines.
- Demonstrated ability to lead complex ML delivery projects, define technical direction, and drive teams toward production-quality releases.
- Strong player-coach capability, with experience mentoring technical teams while remaining hands-on in modeling and experimentation.
- Ability to translate ambiguous scientific problems into structured technical plans and execution roadmaps.
- Strong collaboration skills across research, product, engineering, and scientific stakeholders.
Responsibilities
- Lead the end-to-end delivery of federated co-folding and structural biology model systems, staying deeply involved in modeling, architecture, evaluation, and engineering execution.
- Design, fine-tune, and extend large-scale foundation models for structural biology, including systems such as OpenFold, Boltz-2, and ESMFold, ensuring robust and production-ready outputs.
- Translate high-level scientific and technical objectives into clear execution plans, workstreams, and delivery milestones.
- Define and enforce model evaluation criteria, ensuring high-quality, validated results suitable for real-world drug discovery applications.
- Own delivery timelines and ensure model releases are shipped reliably, managing risks, dependencies, and technical trade-offs proactively.
- Align consortium and internal stakeholders on objectives, data requirements, evaluation frameworks, and delivery expectations.
- Collaborate closely with product, research, engineering, and leadership teams to ensure model development aligns with platform and customer needs.
- Mentor ML engineers and scientists while contributing directly to technical design, experimentation, and system architecture.
- Continuously surface and address blockers, bugs, and risks with clear, actionable recommendations.
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