Applied Machine Learning Engineer

Who are we?

Sweep is an agentic workspace for Salesforce that makes complex configurations simple to see, govern, and scale. Our metadata agents keep context alive so AI delivers real business value. The result: clarity, control, and speed — by design.

What will you do at Sweep?

As an Applied Machine Learning Engineer at Sweep, you’ll play a key role in designing and delivering ML-driven solutions that go directly into production. This is a hands-on, engineering-focused role where you’ll collaborate closely with product and backend teams to bring intelligent features to life.

• Build, test, and deploy end-to-end ML systems – from data exploration and prototyping to production-grade deployment
• Write high-quality, maintainable Python code, including backend logic (not just model code)
• Collaborate with product managers and engineers to align ML solutions with user and business needs
• Work with real-world datasets to solve practical problems, focusing on impact and scalability
• Participate in system design discussions, including how cloud infrastructure supports scalable ML
• Explore and integrate Generative AI capabilities where relevant
• Help shape internal ML best practices and contribute to team knowledge-sharing
  
  

• Has a strong engineering background – experience as a Software Engineer or Machine Learning Engineer
• Is proficient in Python and has written production-level backend code, not just ML scripts
• Understands and can write SQL; familiarity with JavaScript is a plus
• Has experience working with cloud infrastructure and understands system architecture at scale
• Is curious and up to date with Generative AI tools and frameworks (e.g. Langchain, Langraph, Mastra, etc)
• Is practical and impact-driven – focused on shipping and solving real problems
• Communicates clearly and works well in cross-functional teams
• Has deployed ML models into production environments
• (Nice to have) Has experience with ML Ops, model monitoring, or experimentation platforms
• (Nice to have) Has worked in fast-moving, startup-like environments