About the Role
WHAT MAKES US A GREAT PLACE TO WORKWe are proud to be consistently recognized as one of the world's best places to work. We are currently the top ranked consulting firm on Glassdoor's Best Places to Work list and have earned the #1 overall spot a record seven times.Extraordinary teams are at the heart of our business strategy, but these don't happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.
WHO YOU'LL WORK WITHAs the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making. The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.WHERE YOU'LL FIT WITHIN THE TEAMAI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands-on, growth-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.WHAT YOU'LL DOCore ML and Data Pipeline Engineering (65%)
• Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers, under the direction of senior engineers.
• Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic, following established patterns.
• Write production-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.
• Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.
• Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high-severity issues to senior engineers.Collaboration and Support (25%)
• Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.
• Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.
• Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade-offs.
• Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.Other (10%):
• Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.
• Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.
• Take on interviewing and hiring-loop participation as your experience grows.ABOUT YOU
• Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).
• 2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.
• Exposure to model deployment, serving, or monitoring is a plus.
• Experience working with structured feedback and code review, and a track record of improving code quality over time.
• Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.
• Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.
• Demonstrated ability to take a well-scoped task from specificatio