Why This Job is Featured on The SaaS Jobs
This Applied Machine Learning Engineer role stands out in SaaS because it sits directly on the product line: building ML systems that ship into a Salesforce-adjacent, workflow-heavy environment. In practice, that means working where model behavior meets enterprise configuration, governance, and reliability—an area where many SaaS companies struggle to translate “AI capability” into repeatable customer value. The emphasis on production deployment and backend-quality code signals a product-engineering approach to ML rather than a research sandbox.
For a SaaS career, the durable upside is exposure to end-to-end ML delivery: data exploration, prototyping, deployment, and ongoing system design in cloud infrastructure. That combination is increasingly central as SaaS teams integrate generative AI while keeping observability, scalability, and maintainability in view. Experience partnering with product and backend functions also builds the cross-functional judgment that transfers well across B2B SaaS organizations.
This position is best suited to engineers who prefer owning the full lifecycle of ML features and are comfortable treating models as part of a larger software system. It fits someone who enjoys pragmatic problem selection, clear communication with product stakeholders, and iterating toward measurable impact in real-world datasets.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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
We are looking for someone who:
- 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
About Sweep:
As a fast-growing, venture-backed startup, we are proud to be supported by top investors like Insight Partners and Bessemer Venture Partners. With teams in New York, Portugal, and Tel Aviv, we are a passionate, success-driven group that thrives on collaboration and innovation.
Join us to be part of a dynamic, people-first community where we tackle complex challenges, take smart risks, and celebrate each other's successes. Learn more about our mission and culture on our About page https://www.sweep.io/about.
