Principal ML Scientist

We are seeking a Principal Machine Learning Scientist with deep expertise in multimodal data (e.g., genomic, transcriptomic, clinical, and/or imaging) to help us unlock transformative insights in precision medicine. In this senior technical leader role, you will lead the development of novel machine learning and AI approaches to power next-generation insights in precision medicine, translational research, and drug development.

You will partner with scientists, engineers, and product teams to translate cutting-edge Machine Learning into real-world impact, spanning research, development, and production environments. You will have the opportunity to drive scientific innovation with direct translational impact, collaborate with a multidisciplinary team of domain experts, clinicians, and technologists and access to unique, large-scale multimodal datasets and high-performance computing infrastructure.

Responsibilities

- Lead the design and development of machine learning models that integrate multimodal data sources (e.g., WGS/WES, RNA-seq, clinical records, pathology images, real-world data).

- Develop novel algorithms and adapt state-of-the-art architectures (e.g., transformers, graph neural networks, diffusion models, embedding models) for biological and clinical applications.

- Collaborate with cross-functional teams to define scientific questions, shape the ML roadmap, and prioritize data problems aligned with our strategic objectives.

- Design and run rigorous experiments to evaluate model performance, interpretability, robustness, and fairness in high-stakes biomedical contexts.

- Guide the creation of scalable, reproducible pipelines for training, evaluating, and deploying models, serving as a bridge between research innovation and production deployment.

- Provide technical leadership and mentorship to junior engineers and scientists, contributing to a culture of scientific and technical excellence.

- Publish high-impact research and contribute to open-source projects or industry consortia to extend the impact of your work.

Requirements

- PhD in Computer Science, Computational Biology, Bioinformatics, Statistics, or related field, or equivalent practical experience.

- 10+ years of experience applying machine learning to real-world problems, including at least 4+ years in biology (e.g., genomics, transcriptomics, proteomics) or precision medicine domains.

- Expertise in handling and modeling large-scale, high-dimensional biological data (e.g., sequencing, imaging, clinical records).

- Deep understanding of modern ML/AI methods (e.g., deep learning, probabilistic models, representation learning) and their application to complex real-world data.

- Experience integrating and modeling multimodal dataset (e.g., genomics + clinical or imaging data).

- Strong programming skills and familiarity with ML frameworks (e.g., PyTorch, TensorFlow, Pandas/NumPy, MLFlow).

- Proven ability to work across disciplines and communicate complex results clearly to technical and non-technical audiences.

Preferred Qualifications

- Experience with modern ML architectures, including transformer-based models and/or alternatives suited for biological, multimodal, or structured data.

- Contributions to scientific publications, preprints, or open-source projects in the genomics/ML space.

- Experience with building or deploying ML systems using cloud-based infrastructure (e.g., AWS)