Staff SW Engineer, DevOps/MLOps

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.

Our team hails from a broad range of disciplines and is committed to the company’s mission to create equitable access to clinical trials for any patient, anywhere. Join us, and bring your expertise, passion, creativity, and drive as we work together to realize this mission.

Paradigm is looking for a software engineer to level up our DevOps and MLOps practices.  You will be responsible for designing, developing and optimizing both our software and ML infrastructure, with goals to enable rapid prototyping, development and deployment of our products.

As a Staff Software Engineer, you will be responsible for designing, developing, and optimizing the ML infrastructure that supports large-scale data processing, model training, and deployment of both models and applications. Your work will ensure that our ML systems operate seamlessly, securely, and efficiently, helping power intelligent clinical trial solutions.

In addition, you’ll level up Paradigm’s overall DevOps practice.  As part of our engineering team, you’ll contribute to how we deploy, monitor, and alert on our applications, APIs.  You’ll bring production SRE practices across our organization, with the goal of accelerating our engineering, data science, and machine learning teams.  

What you'll do:

• Infrastructure Development & Optimization: Architect and implement robust, scalable ML infrastructure that supports model training, deployment, and monitoring.
• ML Platform Engineering: Develop and maintain ML model serving and orchestration platforms, ensuring seamless integration with existing engineering workflows. Gitlab pipelines for software and machine learning engineering
• Data Pipeline and Feature Engineering: Design and optimize ETL/ELT pipelines for ML applications, enabling efficient and reliable data preprocessing and transformation.
• MLOps and Automation: Implement MLOps best practices to streamline model lifecycle management, from training to deployment, monitoring, and retraining.
• Cloud & Containerization: Leverage cloud computing resources (AWS, GCP) and container orchestration (Docker, Kubernetes) to scale ML workloads efficiently.
• Monitoring and Reliability: Develop advanced monitoring systems to track model performance, data drift, and infrastructure health.
• Security & Compliance: Collaborate with privacy and security teams to ensure compliance with regulatory standards and best practices for handling sensitive clinical data.
• 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.

What you'll bring:

• Background in Production Distributed Systems: You’ve worked with complicated distributed systems, and understand how to deploy, monitor, and appropriately alert on these systems in production.
• Extensive ML Infrastructure Experience: 4+ years of experience in machine learning infrastructure, data engineering, or distributed systems, with a strong focus on building scalable, high-performance ML platforms.
• Strong ML Workflow Expertise: Deep understanding of ML pipeline orchestration, model deployment, and monitoring in production environments.
• Cloud and MLOps Proficiency: Hands-on experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI) and orchestration tools (Kubeflow, Airflow, or Dagster).
• Programming & Automation Skills: Proficiency in Python, SQL, and infrastructure-as-code (Terraform, CloudFormation) to automate ML workflows.
• Scalable Data Processing: Experience with distributed data processing frameworks such as Apache Spark, Ray, or Dask for handling large-scale ML datasets.
• Containerization & DevOps: Strong background in Docker, Kubernetes, CI/CD, and monitoring tools (Prometheus, Grafana) for infrastructure man