The Opportunity

Imaging based phenotyping of in-vitro biology is at the heart of insitro's efforts to accelerate drug development. At insitro, we use machine learning to derive clinically relevant insights from rich datasets generated in-house.

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. This is a hybrid position that requires you to be in our South San Francisco headquarters at least three days per week. Join us, and help make a difference to patients!

Responsibilities

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

About You

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

Nice to Have

Demonstrated ability to write software in a team, industry experience or substantial involvement with open source projects.

Experience modeling with video or imaging time-series datasets

Experience with microscopy hardware, and/or image data acquisition in other contexts

Understanding of optical physics, resolution, numerical aperture, etc.

Familiarity with cloud computing services (e.g., AWS or Azure)

Experience with database languages (e.g., SQL)

Publication record of meaningful contributions to high-quality work in relevant computer vision, clinical ML, or biomedical venues

Compensation & Benefits at insitro

Our target starting salary for successful US-based applicants for this role is $215,000 - $235,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.