Senior Member of Technical Staff -Machine Learning

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 Bangalore, India and requires 5–8 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.

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.

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).
• 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 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