Senior Machine Learning Engineer

You’ll work on applying modern machine learning to one of the hardest real-world problems: detecting disease from biological signals.

This is not product analytics, ad ranking, or recommendation systems — the work directly supports scientific discovery and clinical impact.

You’ll collaborate with researchers across computational biology, molecular science, and ML engineering to design experiments, train large-scale models, and translate model behavior into meaningful biological insight.

• Develop and train ML/DL models on complex biological datasets (genomics / molecular signals)
• Design experiments to validate model generalization and robustness
• Improve interpretability to understand biological mechanisms behind predictions
• Train and optimize large-scale models using modern distributed infrastructure
• Partner with engineering teams to ensure reproducible and scalable research workflows
• Contribute to research that moves from experimentation → real-world application

• Industry or post-doctoral experience applying ML/DL in research environments
• Strong theoretical and practical ML knowledge
• Training large models in distributed environments
• Performance optimization for model training workloads
• Modern training infrastructure (Ray, Deep Speed, or equivalent approaches)
• GPU-accelerated workloads and resource efficiency
• ML experimentation platforms and tracking tools

• Classical ML: tree models, probabilistic models, boosting, Bayesian methods
• Deep learning: foundation models / representation learning
• Training paradigms: supervised, self-supervised, contrastive learning
• Proficient in Python and modern ML frameworks (PyTorch preferred)
• Experience working with large datasets and experimental evaluation
• Ability to collaborate with multidisciplinary teams

• Computational biology or genomics exposure
• Bioinformatics pipelines or molecular data