Senior Software Engineer, ML Platform

The Altos Labs Data & ML Platform group is building a high-performance, scalable, flexible machine learning platform for scientific data analysis, visualization, training, and inference across many different kinds of biological data. We will enable and accelerate the Altos science mission by making all our models easily trainable, findable, interpretable, and accessible across diverse research groups.

Responsibilities

• Motivated to build, deploy, and manage diverse kinds of scientific analysis and machine learning workflows in an integrated, usable framework
• Interested in understanding user needs across a wide range of scientific disciplines, and communicating with users to build systems that they can use productively
• Demonstrated software engineering skills in developing reliable, scalable, performant distributed machine learning pipelines and frameworks in a cloud environment
• Experience with all facets of large-scale machine learning systems such as data transformation pipelines, model management & serving, versioning & dependency management, data lakes, feature stores, etc.
• Capable of proposing, building, and owning complex system designs and integrating them into a coherent whole
• Champions maintainable, scalable, and reusable software engineering techniques and acts as an ambassador to promulgate tools and practices to the research community.
• Mentor software engineers and computational scientists, evangelizing best practices around development tools, CI/CD, and other methods to improve code quality and efficiency.

Required Qualifications

• M.S. or Ph.D. in Computer Science, or related quantitative field, or equivalent technical experience
• 3+ years software development experience
• Experience with one or more programming languages commonly used for large-scale machine learning frameworks, such as Python, C, C++, R, Julia, etc., and familiarity with package management systems, such as Conda, PyPi, etc.
• Experience applying software engineering practices to machine learning in a scientific research environment, or another environment with similar characteristics
• Strong knowledge and understanding of machine learning frameworks and MLOps systems, including experience with cloud computing and/or distributed/parallel computing.
• Demonstrated track record of hands-on technical leadership, including owning and delegating technical design decisions.
• Excited to design, implement, and evangelize technical and cultural standards across scientific and technical functions.

The target salary for this role is: $186,150 - $251,850.
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