Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.
Machine Learning Engineer — Senior Member of Technical Staff (Senior Associate)
Role summary:
You will design, build, and deploy advanced machine learning and deep learning solutions that improve clinical and operational outcomes for healthcare providers. This in-person role is based in Pune, India and requires 3–5 years of relevant experience. The successful candidate will collaborate closely with product, data, and platform engineering teams to move models from research to production and will report to the Senior Engineering Manager.
Team summary:
We are looking for a Senior Machine Learning Engineer to join the Data Science team within the Athena Clinicals Division. You will work with the team to develop and deploy machine learning models across a variety of healthcare products and domains.
The data science team is bringing machine learning to bear against the hardest problems in healthcare. We partner with product managers, clinicians, and engineering teams to translate clinical and operational problems into measurable ML use cases. Our work spans the full model lifecycle: exploratory analysis, feature engineering, model development, rigorous evaluation, reproducibility, automated training pipelines, and monitored production deployments. The team applies a range of methods — from traditional supervised learning to modern deep learning and generative AI — to solve problems such as document understanding, clinical NLP, clinician workflows, etc. We place a strong emphasis on model safety, explainability, and measurable impact on care delivery and practice operations. The team works hand-in-hand with platform engineers to deploy state-of-the-art models using cloud technologies and production orchestration (CI/CD, containerization, orchestration), and we aim to make ML deliverable, observable, and maintainable at scale across athenahealth’s product portfolio.
About you:
You enjoy owning end-to-end technical work and solving challenging problems. You keep up with advances in machine learning, especially deep learning and generative AI, and apply them thoughtfully to real-world product needs. You have hands-on experience building, evaluating, and deploying ML and deep learning models, and are comfortable communicating results and trade-offs to technical and non-technical stakeholders. You write clean, well-documented code, and collaborate effectively with cross-functional teams to produce reliable, auditable solutions. You are motivated by improving healthcare outcomes using data and models.
Essential Job Responsibilities:
•Develop production-ready machine learning and deep learning models using Python and relevant libraries.
•Implement and evaluate complex neural network architectures (NLP and/or computer vision) for healthcare use cases.
•Design and build data pipelines and feature engineering workflows
•Integrate models into scalable production environments using containerization and orchestration patterns.
•Optimize model performance, conduct error analysis, and design rigorous validation and monitoring processes.
•Collaborate with product managers, clinicians, and engineers to translate clinical problems into measurable ML solutions and acceptance criteria.
•Evaluate and adopt deep learning frameworks, transformer-based models, and foundational model techniques (LLMs/GenAI) to solve product problems.
•Apply prompt engineering and optimization practices to improve generative AI outputs and alignment with product requirements.
•AI competency expectation: Integrate AI and generative-model capabilities into development workflows—evaluate new AI tools and model variants for product fit, prototype responsible uses, and recommend best practices for safe, reliable deployment of AI features that enhance clinical and operational decision-making.
•Communicate technical trade-offs, design decisions, and model…