Paradigm is rebuilding the clinical research ecosystem by enabling equitable access to trials for all patients. Our platform enhances trial efficiency and reduces the barriers to participation for healthcare providers. Incubated by ARCH Venture Partners and backed by leading healthcare and life sciences investors, Paradigm’s seamless infrastructure implemented at healthcare provider organizations, will bring potentially life-saving therapies to patients faster.
Requirements
- You’ve worked with complicated distributed systems, and understand how to deploy, monitor, and appropriately alert on these systems in production.
- 4+ years of experience in machine learning infrastructure, data engineering, or distributed systems, with a strong focus on building scalable, high-performance ML platforms.
- Deep understanding of ML pipeline orchestration, model deployment, and monitoring in production environments.
- Hands-on experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI) and orchestration tools (Kubeflow, Airflow, or Dagster).
- Proficiency in Python, SQL, and infrastructure-as-code (Terraform, CloudFormation) to automate ML workflows.
- Experience with distributed data processing frameworks such as Apache Spark, Ray, or Dask for handling large-scale ML datasets.
- Strong background in Docker, Kubernetes, CI/CD, and monitoring tools (Prometheus, Grafana) for infrastructure management.
Responsibilities
- Architect and implement robust, scalable ML infrastructure that supports model training, deployment, and monitoring.
- Develop and maintain ML model serving and orchestration platforms, ensuring seamless integration with existing engineering workflows. Gitlab pipelines for software and machine learning engineering
- Design and optimize ETL/ELT pipelines for ML applications, enabling efficient and reliable data preprocessing and transformation.
- Implement MLOps best practices to streamline model lifecycle management, from training to deployment, monitoring, and retraining.
- Leverage cloud computing resources (AWS, GCP) and container orchestration (Docker, Kubernetes) to scale ML workloads efficiently.
- Develop advanced monitoring systems to track model performance, data drift, and infrastructure health.
- Collaborate with privacy and security teams to ensure compliance with regulatory standards and best practices for handling sensitive clinical data.
Other
- Bring your expertise, passion, creativity, and drive as we work together to realize this mission.
- Collaboration & Mentorship: Work closely with software engineers, data scientists, and ML engineers to align infrastructure with business and technical goals while mentoring junior engineers.
- Stay Current on Engineering and ML Infrastructure Trends: Keep up to date with advancements in ML platforms, distributed computing, and scalable ML systems, integrating innovative solutions into our ML ecosystem.
- Strong Problem-Solving & Collaboration Skills: Ability to troubleshoot complex ML infrastructure issues and work cross-functionally with engineers, data scientists, and product teams.
- Startup Experience: Familiarity with early-stage environments and enthusiasm for contributing to high-growth, dynamic initiatives.
