Associate Principal Scientist, OmicsAI
Core
Designing and deploying advanced Machine Learning and AI solutions for processing, analysis, and interpretation of multi-omics data (transcriptomics, proteomics, and cell painting) within toxicological safety domains to accelerate drug development.
Role type
Associate Principal Scientist (hands-on IC with strategic direction)
Builds
Production ML models and platforms for clinical transcriptomics, proteomics, and cell painting data analysis
Domain
Pharmaceutical R&D / Bioinformatics / Toxicology
Deliverable
production ML models
Required skills
Machine vision models, machine learning/AI workflows for bioinformatics, foundation models for transcriptomics or cell painting, complex imaging dataset modeling, compound safety prediction, large high-dimensionality unstructured dataset analysis, CNNs/vision transformers/diffusion models, Python, AI libraries (TensorFlow/PyTorch), MLOps/DevOps practices, cloud environment (AWS)
Preferred skills
Proteomics datasets, Cell Painting vision models, compound safety ML models, complex global organization experience, Agile/product-focused delivery
Technologies
Python, TensorFlow, PyTorch, CNNs, Vision Transformers, Diffusion Models, ResNet, UNet, DINO, CLIP, Stable Diffusion, AWS, GitHub, CI/CD
Responsibilities
Design and deploy ML models for large-scale analysis of clinical transcriptomics, proteomics, and cell painting data; Lead development and implementation of machine vision models in cell painting and advanced cell models from ideation to production; Collaborate with scientists to build platforms underpinning research; Apply data science methodologies to develop novel solutions where off-the-shelf methods do not fit; Work across timezones with global AI research teams; Champion production-first attitude to scale exploratory research; Improve ML development environments, platforms, and tooling.
Seniority
Senior, hands-on IC with strategic direction
