Senior ML Engineer

About the job

Brief Job Overview The Digital product engineering team at USP is seeking a Senior ML Engineer with expertise in building robust Data/ML pipelines, managing large-scale data infrastructure, and enabling advanced analytics capabilities. This role is critical to supporting projects that align with our mission to protect patient safety and improve the health of people around the world. Responsibilities • Design and implement scalable data collection, storage, and processing ML pipelines to support enterprise-wide data needs. • Implement and maintain data governance frameworks and data quality checks within data pipelines to ensure compliance and reliability. • Build and optimize data models and data pipelines to support self-service analytics and reporting tools such as Tableau, Looker, and Power BI. • Collaborate with data scientists to operationalize machine learning models by integrating them into production data pipelines and ensuring scalability and performance. • Develop and manage ETL/ELT workflows and orchestration using tools like Airflow or AWS Step Functions to ensure efficient data movement and transformation. • Implement CI/CD practices for ML models and data pipelines, including automated testing, containerization, and deployment Who USP is Looking For? The Successful Candidate Will Have a Demonstrated Understanding Of Our Mission, Commitment To Excellence Through Inclusive And Equitable Behaviors And Practices, Ability To Quickly Build Credibility With Stakeholders, Along With The Following Competencies And Experience Education Bachelor’s degree in relevant field (e.g. Engineering, Analytics or Data Science, Computer Science, Statistics) or equivalent experience. Experience • 3+ years of experience designing, building, and optimizing large-scale data platforms and pipelines for structured, semi-structured, and unstructured data. • Expert in ETL/ELT workflows, data ingestion (streaming, batch, APIs), and distributed processing using Apache Spark, PySpark, Airflow, Glue, and modern orchestration frameworks. • Strong experience architecting and integrating data across heterogeneous systems, including data lakes and warehouses (AWS S3, Redshift, Snowflake, Delta Lake). • Deep knowledge of data quality frameworks, data governance, metadata management, and SQL optimization for analytical workloads. • Advanced Python/PySpark skills with hands-on experience in data processing and API development (FastAPI, Flask, Django). • Deep expertise in AWS services including S3, RDS, Redshift, Lambda, Step Functions, SageMaker, EC2/ECR, CloudWatch, ALB/NLB, and autoscaling. • Strong foundation in ML system design, feature stores, model registries, A/B testing, and deploying ML models with high availability and autoscaling.