Machine Learning Engineer III – ML Core

Machine Learning Engineer III – ML Core

Requirements

• Bachelor’s or Master’s degree plus 3-5 years of professional experience with a focus on artificial intelligence and machine learning.
• Proven track record in developing, deploying, and maintaining state-of-the-art ML models in scalable production environments.
• Strong experience in building and managing large-scale ML pipelines, feature and embedding stores, model monitoring, and performance evaluation systems.
• Hands-on proficiency with MLOps technologies such as Kubernetes, AWS, Docker, Terraform, Helm, Kafka, Airflow, Kubeflow, Argo, Ray, and GitLab/GitHub Actions for end-to-end ML lifecycle management.
• Excellent proficiency in Deep Learning frameworks (Pytorch/Tensorflow), Python (including Scipy, Numpy, Pandas) and software engineering skills. Strong analytical and quantitative skills.
• Knowledge of recent advances in machine learning and computer vision concepts, including but not limited to transformer-based models, self-supervised learning, advanced segmentation models, inference-optimization techniques.
• Strong communication skills and the ability to collaborate effectively in a cross-functional environment.
• Intellectual curiosity and the ability to learn quickly in a complex space.

Responsibilities

• Design, build, and optimize ML infrastructure and platforms supporting foundational base models, enabling rapid iteration from training to deployment.
• Develop robust and scalable tooling to streamline end-to-end ML workflows including data ingestion, dataset creation & management, automated training, comprehensive evaluation, and seamless model deployment.
• Collaborate closely with ML engineers, researchers, software engineers, and product managers to effectively integrate and deploy innovative ML methods and base models into production systems.
• Implement and optimize automated ML operations through advanced CI/CD pipelines, leveraging technologies like Kubernetes, Docker, Helm, and Airflow for orchestration and infrastructure-as-code.
• Standardize and enhance model performance, reliability, data efficiency, and inference latency across diverse ML use-cases, utilizing rigorous benchmarking and iterative improvement methodologies.
• Engage in technical research and prototyping to evaluate new tools, frameworks, and methodologies that enhance MLOps and applied ML capabilities.
• Champion best practices, contribute to documentation, mentor team members, and participate actively in knowledge sharing and continuous improvement initiatives.

Preferred Qualifications

• Publications or research in fields related to machine learning and biomedical science are a bonus.
• Proficiency in using AI Assistants for code development (Cursor, CoPilot) is a bonus.