Scientist, Machine Learning

What You Will Contribute To Altos

The Altos Labs is building high-performance, scalable, quantitative solutions for biomedical image analysis and integration with multi-Omics data. The team work at multiple scales including data from Electron/Light Microscopy, Digital Histology and Pathology up to functional analysis In Vivo. We will enable and accelerate the Altos mission by leveraging state of the art computer vision and machine learning, and collaborating with MLOps at Altos to make all our models easily trainable, findable, interpretable, and accessible across diverse research groups.

Responsibilities

• Develop Generative AI models for imaging and multi-omics data integration and cross domain mapping of data collected in situ and in vivo.
• Demonstrate software engineering skills to develop reliable, scalable, performant distributed systems in a cloud environment.
• Build, deploy, and manage multi modal analysis pipelines for scientific analysis, and machine learning workflows in an integrated, usable framework.
• Understand scientists’ needs across a wide range of scientific disciplines by collaborating with both users and software engineers.
• Bridge  the communication gap between experimental scientists, algorithm developers and software deployers.

Who You Are

Minimum Qualifications

• PhD in Computer Science/Biomedical Engineering or related quantitative field.
• Candidates should have 0-5 years of relevant industry and/or academic experience.
• Experience with one or more programming languages commonly used for large-scale data management and machine learning, such as Python, C++, Pytorch/Tensorfllow, Pytorch Lightning etc.
• Previous experience with Machine Learning at scale: Large Language Models and Self-Supervised/Contrastive/Representation Learning for Computer Vision applications and multi modal integration.
• Experience applying software engineering practices in a scientific environment, or another environment with similar characteristics.
• Demonstrated track record of hands-on technical leadership and scientific contributions such as papers or conference communications.
• Excited to design and implement technical and cultural standards across scientific and technical functions.

Preferred Qualifications

• Bioinformatics data processing and analysis.
• Multi source data Integration.
• Experience with cloud computing and containerization.
• Knowledge of genetics/human genetics.