Staff SW Engineer, DevOps/MLOps

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 management.
• Security Compliance Awareness: Knowledge of best practices for data governance, security, and regulatory compliance, particularly in healthcare or life sciences.
• Strong Problem-Solving Collaboration Skills: Ability to troubleshoot complex ML infrastructure issues and work cross-functionally with engineers, data scientists, and product teams.

Nice to have's:

• Experience in Healthcare/Clinical Data: Background in working with EHR, lab data, or clinical trial data.
• GenAI/LLM Infrastructure: Experience optimizing and deploying LLM-based applications in production.
• Startup Experience: Familiarity with early-stage environments and enthusiasm for contributing to high-growth, dynamic initiatives.

The base compensation range is $180,000 – $220,000 USD per year. Actual salaries will vary based on candidates' qualifications, skills, and location.