Senior Machine Learning Engineer

Productionize research models by improving reliability, scalability, testing, and maintainability

Establish MLOps practices for experiment tracking, model/version management, validation, deployment, and rollback

Implement CI/CD pipelines and software engineering best practices to ensure code quality, maintainability, security, and reproducibility across ML applications

Monitor model performance, data quality, data/model drift, service health, usage and troubleshoot issues

Build and maintain reproducible workflows for data preparation, model training, inference, and evaluation in collaboration with ML scientists

Evaluate and implement emerging MLOps, model observability, and ML platform technologies that improve deployment speed, reliability, and scalability

Communicate technical designs, trade-offs, metrics, and recommendations to technical and scientific partners

What we expect of you

We are all different, yet we all use our unique contributions to serve patients. The collaborative professional we seek is a Software/ML Engineer with these qualifications.

Basic Qualifications

Doctorate degree with 4+yrs in Data Science, Computer Science, Computational Biology, Bioinformatics, Computational Chemistry, or a related field

Or

Master's degree and 8+ years of directly related experience

Preferred Qualifications

Experience building and supporting production ML systems, model-serving platforms, APIs, or data-driven applications

Strong Python programming and software engineering fundamentals, including testing, code review, documentation, packaging, and version control

Hands-on experience with MLOps tools such as MLflow, model registries, experiment tracking, CI/CD and model lifecycle management

Experience with Docker, Kubernetes, REST/gRPC APIs, and cloud-native deployment patterns

Familiarity with AWS, Databricks, Spark, or similar cloud/data platforms

Experience with model observability, logging, alerting, drift detection, and production troubleshooting

Familiarity with machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or related libraries, and the ability to package models for reliable inference

Ability to work effectively with scientists, ML researchers, data engineers, platform teams, and software engineers

Strong ownership, problem-solving, and communication skills, with demonstrated contributions to production ML systems, open-source MLOps tools, or publications in venues such as MLSys, NeurIPS, ICML, ICLR, or comparable venues; candidates should highlight representative work on their resume.

.