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 Senior Machine Learning Engineer on the Data Science team within the Clinicals division. In this in-person role based in Bangalore, India, you will design, build, and deploy machine learning and deep learning solutions that improve clinical and operational outcomes for healthcare providers. You will partner with product, clinical, data, and engineering teams to move models from research into production and help ensure they are reliable, measurable, and maintainable. This role 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 related libraries.
- Implement and evaluate neural network architectures for natural language processing and computer vision use cases in healthcare.
- 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 validation and monitoring processes.
- Collaborate with product managers, clinicians, and engineers to translate clinical problems into measurable machine learning solutions and acceptance criteria.
- Evaluate deep learning frameworks, transformer-based models, and foundation model approaches, including large language models and generative AI, to solve product problems.
- Apply prompt design and testing practices to improve generative AI outputs and align them with product requirements.
- Use AI tools in development and analysis workflows to speed experimentation, compare model outputs, and review results, while validating findings before they are used in production decisions.
Additional Job Responsibilities:
- Research and prototype model architectures or training strategies relevant to product goals.
- Support model fine-tuning and transfer learning workflows for domain-specific large language 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 with product and annotation partners.
- Contribute to documentation for model governance, reproducibility, and on-call support.
- Mentor junior engineers and support knowledge sharing within the team.
- Assist with performance tuning and cost optimization for training and inference workloads.
- Participate in security and privacy reviews related to model data and deployment.
- Contribute to discussions on machine learning 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.
- 5-8 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 NLP or computer vision libraries.
- Experience with large language models, generative AI techniques, prompt design, and model fine-tuning.
- Familiarity with NLP or computer vision techniques and evaluation metrics.
- Experience with cloud environments and infrastructure is preferred familiarity with AWS, Kubernetes, Kubeflow, or EKS is a plus.
