Machine Learning Engineer
Core
Build machine-learning systems to remove bottlenecks in drug discovery and development, bridging research and engineering to accelerate experiments and decision quality.
Role type
Senior IC machine-learning engineer (biomolecular modeling & drug discovery)
Builds
Systems for experiment planning, literature triage, protocol drafting, in silico screening, candidate generation, filtering, predictive analysis, and active-learning loops connecting predictions to wet-lab results.
Domain
Biopharma / Drug Discovery / Biomolecular Modeling
Deliverable
production ML models
Required skills
Strong research judgment in biomolecular modeling, ability to own ambiguous problems end-to-end, practical interest in wet-lab constraints, experience with active learning, data-constrained biological modeling, multimodal omics, imaging, or phenotypic data
Preferred skills
Experience with production-scale agent platforms
Technologies
ESM, AlphaFold-family models, RFdiffusion, ProteinMPNN, molecular dynamics, post-training, probing, evaluation
Responsibilities
Identify high-value bottlenecks in drug discovery where ML can improve speed or decision quality; Build systems for experiment planning, literature triage, protocol drafting, in silico screening, candidate generation, filtering, and predictive analysis; Fine-tune and apply biomolecular models (ESM, AlphaFold, RFdiffusion, ProteinMPNN) using company data; Develop candidate-analysis workflows including molecular dynamics and evaluation; Work directly with wet-lab scientists to automate preclinical/clinical workflows; Create internal evaluations measuring model impact on experimental throughput and candidate quality
Seniority
Senior, hands-on IC
