Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.

Position Summary:

Join athenahealth as a Machine Learning Engineer (Associate level) and help build production AI/ML solutions that improve healthcare outcomes. This in-person role is based in Pune, India, and focuses on developing, evaluating, and deploying machine learning models across clinical and operational products. The role requires collaboration with product, engineering, and data teams to move models from prototype to production. This position reports to the Senior Engineering Manager.

About the Team

The Data Science team develops machine learning solutions for healthcare products and workflows. The team works with product managers, clinicians, and engineers to turn clinical and operational problems into measurable machine learning use cases. Work spans the full model lifecycle, including exploratory analysis, feature engineering, model development, evaluation, reproducibility, automated training pipelines, and monitored production deployment. The team uses a range of methods, including supervised learning, deep learning, and generative AI, to support use cases such as document understanding, clinical natural language processing, and workflow improvement. The team also partners closely with platform engineers to deploy models using cloud technologies and production orchestration so that machine learning is scalable, observable, and maintainable across the product portfolio.

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 limitations clearly in writing and presentations.

Additional Job Responsibilities:

• Research and prototype novel model architectures or training strategies relevant to product goals.
• Support model fine-tuning and transfer learning workflows for domain-specific LLM models.
• Contribute to internal tooling and shared libraries for reproducible training and evaluation.
• Participate in design reviews, code reviews, and cross-team technical discussions.
• Help define data collection and labeling priorities in partnership with product and annotation teams.
• Contribute to documentation for model governance, reproducibility, and runbooks for on-call support.
• Mentor junior engineers and contribute to knowledge sharing within the team.
• Assist in performance tuning and cost optimization for training and inference workloads.
• Participate in security and privacy reviews related to model data and deployment
• Attend and contribute to community discussions on ML safety, fairness, and responsible AI practices.

Expected Education & Experience:

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related field (or equivalent practical experience).
• 2–3 years of hands-on experience building and deploying machine learning or deep learning models in production.
• Proficiency in Python, SQL, and Unix/Linux environments.
• Experience developing and implementing deep learning models with complex neural network architectures.
• Familiarity with deep learning frameworks (such as PyTorch or TensorFlow), transformer models, and libraries for NLP/vision.
• Experience with LLMs, generative AI techniques, and prompt engineering; training and fine-tuning LLMs.
• Familiarity with NLP or computer vision techniques and evaluation metrics.
• Experience with cloud environments and infrastructure is beneficial; familiarity with AWS, Kubernetes, Kubeflow, or EKS is a plus.
• Strong verbal and written communication skills for cross-functional collaboration and stakeholder-facing documentation.