Senior Machine Learning Scientist, Insitro

• Ph.D. in computer vision, machine learning, computer science or a related discipline, or equivalent practical experience (e.g., a Master's degree and 2+ years of relevant industry experience)
• Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions on a daily basis
• Demonstrated ability to identify, understand, and find novel applications of state-of-the-art ML/CV methods from literature or other sources
• Strong programming skills in Python, including working with TB-scale image datasets and leveraging modern agentic software tools to accelerate development
• Experience developing models for diverse computer vision tasks (e.g. segmentation, recognition, classification, domain adaptation) including using modern deep learning frameworks (PyTorch, TensorFlow, Keras, etc)
• Extensive hands on experience working with microscopy data or similar biomedical or physical imaging modalities
• Passion for making a difference in the world

Desirable

• As an Imaging ML Scientist, you will develop ML-empowered computer vision pipelines to extract insights about disease mechanisms from multiple microscopy modalities
• You will be part of a cross-functional team of life scientists, software engineers, computational biologists, and machine learning scientists that strive to identify therapeutic targets and develop drugs of high efficacy and low toxicity
• You will be joining a vibrant biotech startup that is in a high growth phase, with promising multiple pre-clinical drug targets in areas such as ALS and metabolic disease
• A lot can change in this exciting phase, providing many opportunities for significant impact
• You will work closely with a very talented team, learn a broad range of skills, and help shape insitro's culture, strategic direction, and outcomes
• This role will be reporting to the Director of Imaging, Cellular Machine Learning
• Partner with experimental and computational biologists to design, troubleshoot, and optimize high-throughput imaging-based experiments and workflows
• Identify, understand, develop, and deploy novel computer vision and machine learning methods such as segmentation, feature extraction, and representation learning to extract features from microscopy image datasets
• Work closely with software engineers to build robust and well-tested image analysis workflows, able to be used by experimental and computational biologists with minimal direct support
• Calibrate analysis tools and workflows, define performance metrics, and conduct benchmarking to select fit-for-purpose solutions
• Communicate findings to cross-functional stakeholders through reports, visualizations, presentations, and publications
• Identify novel disease-relevant phenotypes and propose new screening paradigms that translate to actionable program decisions