Careers

(Senior) Machine Learning Scientist - Computer Vision / Microscopy at Insitro
South San Francisco, CA, US

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 microscopy image datasets of cellular disease model systems and learn connections to patient disease phenotypes. As a Machine Learning Scientist with expertise in microscopy image analysis you will lead the development of cutting edge ML approaches to analyze data from multiple microscopy technologies and platforms. You will integrate in vitro imaging data produced in our lab with imaging studies of large-scale human cohorts to extract insights about disease mechanisms. You will also work closely with a cross-functional team of life scientists, bioengineers and machine learning scientists to integrate imaging data with other data modalities (such as clinical data and transcriptomics) to identify therapeutic targets and develop drugs that have high efficacy and low toxicity.

You will be joining as the founding team of a biotech startup that has long-term stability due to significant funding, but yet is very much in formation. A lot can change in this early and 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. 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

  • 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)

  • Experience applying or developing computer vision models using modern deep learning frameworks (TensorFlow, PyTorch, Keras, etc)

  • Proficiency in working with large-scale image datasets in Linux/Bash and Python

  • Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions

  • Passion for making a difference in the world

 

Nice to Have

  • Experience working with OpenCV, CUDA, OpenGL, etc

  • Experience working with various image file formats

  • Experience with microscopy data acquisition

  • Experience working with histopathology images

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

  • 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

About insitro
insitro is an exciting startup company that aims to take a new approach to drug development: one with big data and machine learning at its core. We plan to build on the ground-breaking innovations that have occurred in life sciences to develop large data sets that are designed from the start to allow machine learning to address fundamental bottlenecks in the drug development process. Our goal is to cure more people, sooner, and at a much lower cost.
 
We are fortunate to have the strong support from the top investors in both biotech and tech: ARCH Ventures, Foresite Capital, A16Z, GV, and Third Rock Ventures. We are building a remarkable team that embodies a new type of culture, one based on a true partnership between scientists, engineers, and data scientists. Together we are working to define the problems, design experiments, analyze the data, and derive the insights that will lead us to new therapeutics. Join us, and help make a difference to patients!

 

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