Senior Software Engineer, Machine Learning

Key Skills

AWS, AWS Sagemaker, Docker, Github, Machine learning techniques, MLOps tools, PyTorch, SLA, Software Development

Job Description

Rad AI is the fastest growing radiologist-led AI company on the market. In addition to winning the 2021 award for “Best New Radiology Vendor” from AuntMinnie, Rad AI ended the year by closing a $25M Series A round and being named to the 2021 CB Insights Digital Health 150 - List of Most Innovative Digital Health Startups. Rad AI continued the momentum by being recognized as 2022 CB Insights AI 100 - Most Promising AI Companies.

Why we're hiring for this role: We are looking for a passionate and experienced Software Engineer versed in building and deploying ML systems efficiently at scale. This is a very special opportunity for the right engineer to work on enormously impactful problems powered by real-world deep learning. As one of the first software engineering hires into the ML team, you will have a great latitude in shaping engineering decisions.

This is what you’ll do:

• Server side software design and implementation for machine learnings systems.

• Collaborate with researchers and data scientists to productionalize machine learning models for scale.

• Building CI/CD pipelines to streamline training and deployment.

• Work on building a robust ML infrastructure that accelerates ML team output and enables continuous improvement - orchestration of pipelines, model versioning, data versioning, model monitoring, model serving, experimentation etc

• Bring in the best software engineering practices to a growing ML organization

This what you'll need:

• Deep understanding of Python and its core concepts. Expertise in writing “Pythonic” code and understanding of what that entails. In-depth grasp of the open-source Python landscape, use tools and libraries with comfort to build applications.

• Proficient in designing and developing RESTful web services, with a solid comprehension of stateless APIs and OpenAPI standards.

• 5+ years of software engineering with versatile experience in building ML-powered production systems.

• Have a solid grasp on the MLOps ecosystem.

• You have acquired best practices to lead the team towards building a robust ML infrastructure and you obsess about SLAs.

• Skilled in leveraging AWS services, integrating with them, and developing in hybrid environments that combine AWS services with custom applications.

• Proficient in using Docker for delivering containerized applications.

This would be nice to have:

• Experience with training large-scale deep learning models.

• You have built high performance online serving infrastructure