Machine Learning Engineer

Location:

San Francisco

Our client is a leading private equity firm with a dedicated AI team that works directly with portfolio company leadership and the investment team to identify, size, and deliver applied AI opportunities. The team runs like a small, fast-moving internal product shop, shipping pragmatic tools and automations in weeks rather than running enterprise software projects measured in quarters.

They're looking for a builder: someone who can take a rough, high-value idea (an automation, an internal copilot, a data pipeline that turns a recurring manual analysis into a repeatable tool) and ship it end-to-end. You'll work shoulder-to-shoulder with the team's data scientists and directly with portfolio company and investment team stakeholders, not off a backlog of tickets handed down from a PM.

• Design and build internal tools, automations, and data pipelines that scale the team's analyses across a broad portfolio
• Develop applied AI / LLM-powered workflows, such as agents or copilots that automate recurring diligence, reporting, or analytical tasks
• Partner with the data scientists to take models and analyses from prototype to production-grade tool
• Integrate with internal and external data sources and systems as needed
• Help set a pragmatic, ship-fast engineering culture suited to a small, high-leverage team rather than a large engineering org

• 2-3 years in a technical/build capacity at a top-tier consulting firm (e.g. the technology arms of the major strategy consultancies), or 2-3 years as a software engineer with exposure to fast-paced, stakeholder-facing delivery
• Strong full-stack or backend engineering skills, with Python required
• Comfort with modern data infrastructure: SQL, cloud data warehouses, APIs
• A genuine product ownership mentality, comfortable scoping, building, shipping, and iterating on a tool with minimal hand-holding
• Comfort working directly with non-technical business stakeholders to turn a rough idea into a working solution
• Strong data analysis skills
• Experience building end-to-end applications with foundation and open-weight LLMs: agents, copilots, and automation workflows
• Curiosity and a willingness to learn

• Exposure to private equity, consulting, or portfolio operations
• Experience building data pipelines that integrate financial, operational, or workforce data across multiple companies
• Experience with additional languages such as Rust or Go

It's a small, high-leverage team, and your code goes straight into decisions that move real capital rather than onto a roadmap. You own things end-to-end, in an environment that moves at deal speed.

Burns Sheehan Ltd will consider applications based only on skills and ability and will not discriminate on any grounds.