Technical Lead – Large Molecule AI Systems
New
GermanyFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonKubernetesMachine LearningPyTorchMLOps
Requirements
- Advanced degree (PhD, MSc, or equivalent experience) in machine learning, computational biology, structural biology, or a related field.
- 5+ years of experience applying machine learning to complex biological or scientific problems such as antibody engineering, protein design, binder prediction, or drug discovery.
- Strong hands-on expertise in Python and PyTorch, with experience working on or extending large-scale models such as AlphaFold, OpenFold, Boltz, or ESM.
- Proven experience in ML system delivery, including evaluation, training, deployment, and validation of production-ready models.
- Solid understanding of ML infrastructure and MLOps practices, including Kubernetes-based training and distributed workflows.
- Demonstrated ability to lead end-to-end ML projects, define technical direction, and drive teams toward high-quality delivery outcomes.
- Strong capability to define success metrics, validate model quality, and ensure robustness for real-world applications.
- Experience operating in cross-functional environments involving research, engineering, product, and scientific stakeholders.
- Excellent communication skills and ability to turn ambiguous scientific challenges into clear, executable technical plans.
Responsibilities
- Lead the development and delivery of federated large molecule AI systems across domains such as antibody modeling, protein co-folding, binder prediction, and biologics developability.
- Drive the implementation of large-scale biomolecular foundation models, including systems inspired by OpenFold, Boltz-2, and ESM, ensuring reliable and high-quality model releases.
- Translate ambiguous scientific and technical goals into structured execution plans, prioritization frameworks, and clearly defined workstreams.
- Define evaluation strategies, validate model performance, and ensure outputs meet production-grade standards for real-world drug discovery applications.
- Manage risks, dependencies, technical trade-offs, and delivery timelines, providing clear recommendations to stakeholders and leadership.
- Align consortium and cross-functional stakeholders on data requirements, objectives, evaluation criteria, and delivery expectations.
- Collaborate closely with product, engineering, research, and leadership teams to ensure model roadmaps align with application and business needs.
- Act as a player-coach, contributing directly to modeling, experimentation, and architecture decisions while mentoring senior engineers and ML scientists.
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