Computer Vision Machine Learning Engineer

The Opportunity

Imaging based high content phenotyping is at the heart of insitro’s efforts to rethink drug development. Our goal is to use machine learning to extract disease phenotypes from high throughput, high content, microscopy image datasets of cellular disease model systems and identify connections to clinical disease phenotypes. 

As a machine learning engineer with expertise in microscopy image analysis you will develop cutting edge ML approaches to analyze data from multiple microscopy technologies and platforms. You will integrate in vitro imaging data produced at insitro with other data modalities such as transcriptomics and data from large-scale human cohorts to extract insights about disease mechanisms. You will be part of a cross-functional team of life scientists, bioengineers and machine learning scientists to identify therapeutic targets and develop drugs that have high efficacy and low toxicity.

You will be joining an agile and fast growing biotech startup that has long-term stability due to significant funding, but yet is very much in formation. You will have ample 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. Join us, and help make a difference to patients!

About You

• Ph.D. in computational biology, computer science or a related discipline, or equivalent practical experience (e.g., a Master degree plus 2 years in relevant industry experience)
• Demonstrated ability to use and develop cutting edge methods for analyzing imaging data
• Extensive hands on experience working with microscopy data or similar biomedical or biophysical imaging modalities
• Experience developing models for diverse computer vision tasks (e.g. segmentation, recognition, classification, domain adaptation) including using modern deep learning frameworks (TensorFlow, PyTorch, Keras, etc)
• Proficiency in Python and working with large-scale image datasets 
• Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions
• Passion for providing better medicine to patients in need 

Nice to Have

• Demonstrated ability to write software in a team, industry experience or  substantial involvement with open source projects. 
• Experience working with OpenCV, CUDA, OpenGL, etc
• Experience working with various image file formats
• Experience with microscopy data acquisition 
• Good understanding of image formation models (Fourier optics)
• Familiarity with cloud computing services (especially AWS) and workflow management tools or batch scheduling systems (e.g. SLURM)
• Experience with database languages (e.g., SQL) and experience with version control practices and tools (e.g. Git)
• Experience working with histopathology images
• Proficiency in C++ or other compiled, statically-typed languages

Benefits at insitro

• Excellent medical, dental, and vision coverage
• Open vacation policy
• Team lunches (catered daily)
• Commuter benefits
• Paid parental leave
• This role may be based remotely, if preferred

About insitro

insitro is a drug discovery and development company using machine learning and data generation at scale to transform the way that drugs are discovered and delivered to patients. We rely on human genetic cohorts, human-derived cellular disease models, and high-throughput biology and chemistry to identify coherent patient segments, actionable therapeutic targets, and new or existing chemical matter. The goal is to deliver predictive insights to improve the probability of success and reduce the number of costly dead ends along the R&D journey. The company has established enabling collaborations with Gilead in NASH and Bristol Myers Squibb in ALS and is building a pipeline of wholly owned and partnered medicines leveraging its unique insights on patient biomarkers, targets, and molecules. insitro is located in South San Francisco, CA and has raised over $600M from top tech, biotech, and crossover investors since formation in 2018. For more information on insitro, please visit the company’s website at www.insitro.com.