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.

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 s 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.

• 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.
• 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.

• 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, Tensor Flow, XGBoost, PyTorch).
• Hands-on experience deploying models in cloud-based environments (AWS, GCP, Azure, and Databricks).
• Proven ability to manage end-to-end ML life cycles at scale, including data ingestion, training, evaluation, deployment, and monitoring.
• Excellent communication skills and demonstrated ability to influence cross-functional teams.

• Experience working with healthcare claims, EHR, or life sciences datasets.
• Advanced degree (M.S. or Ph.D.) in Computer Science, Data Science, or related technical field.
• Strong knowledge of MLOps practices including CI/CD for ML, automated retraining, and model versioning.
• Experience with deep learning architectures for time series forecasting, sequential data, or hierarchical modeling.
• Proficient in designing evaluation protocols and defining performance metrics to rigorously assess model effectiveness and drive data-driven decision-making.
• Comfortable operating in…