Senior Machine Learning Engineer

About Definitive Healthcare: 

At Definitive Healthcare (NASDAQ: DH), we’re passionate about turning data, analytics, and expertise into meaningful intelligence that helps our customers achieve success and shape the future of healthcare. We empower them to uncover the right markets, opportunities, and people—paving the way for smarter decisions and greater impact. 

Headquartered just outside of Boston, Massachusetts, Definitive Healthcare operates across North America, Europe, and India, supporting a growing global client base of more than 2,400 customers since our founding in 2011.

We’re also a great place to work. In 2024 and 2025, we earned multiple workplace honors, including Built In’s 100 Best Places to Work in Boston (both years), a Stevie Bronze Award for Great Employers, and recognition as a Great Place to Work in India.

We foster a collaborative, inclusive culture where diverse perspectives drive innovation. Through programs like DefinitiveCares and our employee-led affinity groups we strive to promote connection, education, and inclusion. 

We are looking for a Senior Machine Learning Engineer to lead the design and implementation of cutting-edge AI/ML systems that deliver transformative business outcomes. In this role, you will take ownership of end-to-end ML solutions, from architecture and modeling to production and performance optimization.

From architecting end-to-end ML solutions to shaping technical strategy, your work will have a broad and lasting impact on customer experience and operational efficiency. The ideal candidate brings extensive experience in applied machine learning, deep software engineering expertise, and a track record of mentoring teams and delivering production-ready models at scale. This is a high-impact, full-stack ML role that blends research, engineering, and leadership, with the opportunity to shape both the company’s technical foundation and product direction. 

What You'll Do

ML Systems Development & Deployment

• Lead the design and implementation of scalable, production-grade ML systems in cloud environments with a focus on performance, reliability, and reproducibility. 
• Collaborate with product managers and senior stakeholders to define and prioritize ML initiatives aligned with business goals. 

Data Pipeline & Feature Engineering

• Oversee the architecture and evolution of data pipelines for multi-terabyte datasets, ensuring efficiency and reliability.
• Guide the development of high-impact features and label sets across diverse domains such as healthcare and consumer analytics

Experimentation & Model Management

• Lead experimentation strategy, including design of A/B tests, advanced validation methods, and lifecycle management using tools like MLflow and Databricks.
• Drive continual model improvement through advanced techniques such as automated retraining, model decay analysis, and bias mitigation. Innovation & Prototyping
• Champion rapid prototyping and proof-of-concept development to evaluate emerging technologies and ML techniques.
• Lead technical explorations into new ML architectures (e.g., foundation models, causal inference, time series deep learning).

Cross-Functional Collaboration

• Serve as a technical leader and trusted advisor, working closely with product, engineering, data, and executive teams to shape end-to-end ML solutions. Code Quality & Documentation
• Set standards for code quality, performance, and documentation, and mentor junior engineers in best practices

What You'll Bring

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field (or equivalent practical experience).
• 5+ years of industry experience as an ML Engineer, Data Scientist, or Data Engineer, with a focus on deploying and scaling ML systems.
• Deep expertise in Python, SQL, and PySpark for distributed data processing, with proficiency in libraries like scikit-learn, PyTorch, and XGBoost.
• Proven experience designing robust ML pipelines, leveraging tools like MLflow or equivalent.
• Strong command of ML frameworks (e.g., scikit-learn, TensorFlow, XGBoost, PyTorch).
• Hands-on experience deploying models in cloud-based environments (AWS, GCP, Azure, and Databricks).
• Proven ability to manage end-to-end ML lifecycles at scale, including data ingestion, training, evaluation, deployment, and monitoring.
• Excellent communication skills and demonstrated ability to influence cross-functional teams.

Preferred Qualifications