Machine Learning Engineer

Bain Capital Overview:

With approximately $215 billion of assets under management, Bain Capital is one of the world’s leading private investment firms. We create lasting impact for our investors, teams, businesses, and the communities in which we live. Over four decades we have strategically grown our platform to focus on Private Equity, Growth & Venture, Capital Solutions, Credit, and Real Assets. Today, our team includes 1,985+ employees in 24 offices on four continents. 

We partner differently to help people and companies embrace possibility and realize potential. Founded as a private partnership in 1984, we have fostered a culture of innovation, entrepreneurialism, and agility, empowering our people to define and own their career trajectories. Today, our partnership approach enables us to pursue strategic growth, build enduring relationships with a robust external network, and collaborate across our integrated platform to connect the deep and diverse expertise that unlocks breakthrough insights.

Our people are the heart of our advantage. Colleagues at all levels have a seat at the table as they tackle business challenges with a principal investor mindset. By asking incisive questions, respectfully challenging one another, and remaining intellectually agile, we work together to achieve exceptional outcomes. 

For more information visit: Bain Capital

Position Overview

We are seeking a hands-on Machine Learning Engineer (MLE) to build and productionize data and machine learning systems that support investment decision-making at Bain Capital. You will be responsible for the full pipeline, from ingesting raw data to serving real-time or batch predictions, and will work closely with data scientists, data engineers, and investment professionals. The ideal candidate will demonstrate strong software engineering practices, expertise in MLOps, and the ability to communicate effectively with both technical and non-technical stakeholders.

Responsibilities

• Data Engineering: Design, implement, and maintain scalable, well-tested data pipelines using technologies such as Snowflake, dbt, Airflow, and Monte Carlo.
• Model Lifecycle Management: Train, package, deploy, and continuously retrain machine learning models. Track experiments using tools such as MLflow or Weights & Biases.
• Serving & DevOps: Containerize services with Docker, expose inference via FastAPI, and operate workloads on AWS infrastructure managed through Terraform.
• Monitoring & Observability: Instrument pipelines and models to detect data drift, performance regressions, and SLA breaches using DataDog and tools like Evidently.
• Generative AI Enablement: Prototype RAG pipelines that pair LLMs with vector databases like Pinecone. Guide prompt-engineering and evaluation best practices.
• Collaboration & Knowledge Sharing: Translate investment team requirements into technical solutions, document system architecture and runbooks, and mentor team members on machine learning engineering best practices.

Required Skills

• Fluency in Python and SQL, with strong fundamentals in software and data engineering.
• Hands-on experience with Airflow, Snowflake, dbt, and Docker-based containerization or similar tools.
• Proven experience deploying machine learning models to the cloud (preferably AWS) using CI/CD pipelines (e.g., GitHub Actions), infrastructure-as-code tools (e.g., Terraform), and container orchestration platforms (e.g., Kubernetes).
• Practical knowledge of experiment tracking (MLflow or W&B) and observability and data quality stacks (Datadog, Monte Carlo, Great Expectations or equivalents).
• Ability to communicate complex technical concepts clearly to business stakeholders.
• Proficiency with Python machine learning libraries (e.g., scikit-learn, XGBoost) and at least one deep learning framework (e.g., PyTorch or TensorFlow).

Nice to Have

• Familiarity with vector databases (e.g., Pinecone) and RAG architectures.
• Exposure to real-time or streaming data systems (Kafka, Kinesis) and distributed compute frameworks (Spark, Dask).
• Experience in financial services or other high-stakes decision-support environments.
• Working knowledge of React or similar front-end frameworks to support interactive data science applications.

Qualifications

• Bachelor’s degree with relevant experience or Master’s degree in Computer Science, Data Science, or a related field.
• 3+ years designing, building, and operating production ML systems.

Why Join Us

• Opportunity to work with a dynamic team at the forefront of data science innovation within the private investment industry.
• Exposure to a diverse range of industries and business challenges.
• Collaborative, entrepreneurial, and supportive work environment with ample opportunities for professional development and growth.

Compensation:

Expected Annual Base Salary $120,000 – $150,000

Actual base salary will be determined by a wide range of factors including but not limited to role, function, level, experience, qualifications and geographic location. In addition to a competitive base salary, this position may be eligible for a discretionary annual bonus based upon factors such as individual impact, team and firm performance. Bain Capital offers a competitive benefits package designed to support employees’ health, financial security, family needs, and overall well-being.

Bain Capital is an equal opportunity employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.