Machine Learning Engineer

Burns Sheehan; San Francisco (CA); On-site; USD 140,000 - 220,000; Full time

Job summary

Burns Sheehan Limited seeks a Machine Learning Engineer in San Francisco to join a nimble AI team within a leading private equity firm. You will build internal tools and data pipelines, deliver end-to-end AI-powered workflows, and work directly with portfolio company stakeholders and the investment team to ship practical solutions in weeks rather than quarters.

Bring 2–3 years in technical delivery, strong Python, and fluency with SQL, cloud data warehouses, and APIs.

Qualifications

• 2-3 years in a technical/build capacity at top-tier consulting firms or as a software engineer with stakeholder-facing delivery
• Strong Python programming skills and backend experience
• Comfort with modern data infrastructure: SQL, cloud data warehouses, APIs
• Genuine product ownership mentality; ability to scope, build, ship, and iterate with minimal direction
• Comfort communicating with non-technical business stakeholders to turn ideas into working solutions
• Strong data analysis skills; ability to turn data into actionable insights
• Experience building end-to-end applications with foundation/open-weight LLMs: agents, copilots, automation workflows
• Curiosity and willingness to learn

Responsibilities

• Design and build internal tools, automations, and data pipelines that scale analyses across a broad portfolio
• Develop applied AI/LLM-powered workflows, such as agents or copilots for diligence, reporting, or analyses
• Partner with data scientists to move models/analyses from prototype to production-grade tools
• 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

Skills

Python

SQL

APIs

Cloud data warehousing

Product ownership

Data analysis

LLM workflows

End-to-end apps

Tools

Rust

Go

Job description

Machine Learning Engineer

Location: San Francisco

About the Team

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.

The Role

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.

What You'll Do

• 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

What You'll Bring

• 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

Nice to Have

• 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

Why This Role

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.