Job Description

Locations

• Multiple Locations, Multiple Locations, Germany, Multiple Locations, Multiple Locations, Germany
• Cambridge, Cambridgeshire, United Kingdom, Cambridge, Cambridgeshire, United Kingdom

Salary

Required Qualifications

• PhD or equivalent experience in Computer Science, Machine Learning, Applied Mathematics, Computational Biology, or related field. (degree)
• Strong software engineering in Python (packaging, testing, CI), with systems thinking for data‑intensive ML. (degree)
• Deep learning experience (PyTorch/JAX/TensorFlow) and solid foundations in linear algebra, probability, and statistics. (degree)
• Proven experience designing robust data pipelines for large‑scale ML (HPC or cloud). (degree)
• Ability to reason about learning signal and to assess information content of real‑world scientific datasets. (degree)
• Excellent collaboration and communication in interdisciplinary teams. (degree)
• Hands‑on cryo‑EM experience (e.g., map reconstruction, refinement, or pipeline tooling). (degree)
• CUDA or C++ for performance‑critical components; experience with mixed precision and memory‑efficient training. (degree)
• Experience integrating experimental data into ML models (e.g., constraints/priors from cryo‑EM, binding assays, spectroscopy). (degree)
• Familiarity with MD data, structure prediction systems, or protein design work-flows. (degree)
• Experience with cost‑optimization for data collection and cloud utilization; clear track record of building reliable, maintainable research software at scale. (degree)
• Experience with structural biology or molecular biology data/techniques (e.g., cryo‑EM, binding assays, spectroscopy, expression, sequencing) (degree)
• Doctorate (degree)

Responsibilities

• Data integration for structure & dynamics: Build ingestion/curation pipelines for structural/biophysical data (mmCIF/PDB, EM maps/particles, binding/biophysics, spectroscopy); implement map/volume preprocessing (e.g., resolution filtering, normalization) and alignment to model inputs/outputs.
• Cryo‑EM expertise: Operationalize end‑to‑end flows from raw image stacks/particles to 3D maps and model‑ready tensors; interoperate with community formats (e.g., EMDB/EMPIAR, mmCIF) and link to sequences/annotations.
• Signal & information content: Design dataset diagnostics (e.g., mutual‑information‑like measures, effective sample size, SNR proxies) to quantify what data teach the model; build active‑learning loops that maximize learning per euro of data collection time.
• Model‑aware data services: Implement scalable, versioned data services and feature stores that feed training/evaluation; design loaders/augmentations optimized for throughput and correctness (GPU‑aware).
• Training‑at‑scale engineering: Own distributed data pipelines and orchestration for large runs on Azure; profile and tune I/O, storage tiers, data locality, and caching; monitor cost, utilization, and failure modes.
• Quality, governance, and reproducibility: Codify schemas/ontologies, metadata contracts, unit/integration tests, and lineage; automate validation and data drift detection; maintain documentation and examples.
• Partner across disciplines: Work closely with ML researchers, structural biologists, and drug designers; translate experimental constraints into robust computational workflows; communicate clearly and proactively.

Travel Requirements

3 days / week in-office