Lead Machine Learning Engineer
Core
Architect and build robust systems to train, deploy, run inference, and monitor Machine Learning and AI systems at scale.
Role type
Lead Machine Learning Engineer
Builds
Data and Machine Learning products
Domain
Healthcare / Machine Learning Engineering
Deliverable
production ML models
Required skills
Python, scikit-learn, HuggingFace, PyTorch, Tensorflow/Keras, MLlib, MLOps, automated model deployment, model performance monitoring, data drift detection, complex SQL, PySpark, Pandas, Airflow, Git, CI/CD, Docker, Kubernetes, AWS, data APIs, Microservices, event driven systems, Large Language Models (LLMs), generative AI
Preferred skills
data mesh concepts, recommender systems, fraud detection, personalization, marketing science, vector databases, knowledge graphs, Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, EMR, Sagemaker, DataDog, PagerDuty
Technologies
scikit-learn, HuggingFace, PyTorch, Tensorflow/Keras, MLlib, Airflow, PySpark, Pandas, Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, EMR, Sagemaker, DataDog, PagerDuty
Responsibilities
Collaborate with cross-functional partners to build data and Machine Learning products; Architect and build robust systems to train, deploy, run inference, and monitor Machine Learning and AI systems at scale; Champion code quality, reusability, scalability, maintainability, and security; Implement processes and tools to ensure data quality, enforce data governance policies and engineering best practices; Integrate Machine Learning and AI systems with production applications; Innovate with new approaches, staying abreast of current research and latest technologies in the broader ML engineering community
Seniority
Senior, technical leadership & mentorship
