Machine Learning Engineer, Infrastructure

Rad AI

Job Overview

Location

San Francisco

Job Type

Full-time

Full Job Description

📋 Description

• As a Machine Learning Engineer on the MLOps team at Rad AI, you will design and maintain the critical infrastructure that powers AI-driven radiology solutions used by thousands of clinicians daily, directly contributing to improved diagnostic accuracy and reduced physician burnout in healthcare.
• Your work will bridge cutting-edge machine learning research and clinical deployment, ensuring models are reliably served, monitored, and scaled in a HIPAA-compliant, high-availability environment that supports real-world patient impact.
• Day to day, you will architect and maintain scalable ML platforms supporting CI/CD/CT pipelines for model training and deployment, leveraging cloud-native technologies to ensure resilience and performance.
• You will develop cloud-native services and serverless architectures using Python as the primary language, integrating with Kubernetes, Docker, and infrastructure-as-code tools to automate and optimize ML workflows.
• You will partner closely with data scientists and research engineers to design robust data pipelines that feed production ML systems, enabling seamless transition from experimentation to clinical use.
• You will write secure, maintainable code adhering to internal standards for style, security, and observability, including implementing monitoring, logging, and tracing solutions using tools like CloudWatch, Grafana, and NewRelic.
• You will collaborate with Product, Research, and Engineering teams to iterate on features, diagnose system inefficiencies, and lead blameless postmortems to improve system reliability and incident response.
• You will contribute to the full ML lifecycle—from model development and optimization to deployment and observability—ensuring that AI innovations translate into tangible improvements in radiology reporting and patient care.
• Rad AI is a mission-driven, fast-growing healthcare AI company backed by top-tier investors and recognized by CB Insights, Deloitte, and CNBC as a leader in AI innovation, offering a collaborative, transparent, and inclusive culture where your work impacts millions of lives.
• In this role, you will deepen your expertise in MLOps, cloud infrastructure, and healthcare AI while gaining hands-on experience with LLMs, distributed systems, and HIPAA-compliant ML platforms at scale.

🎯 Requirements

• 6+ years of industry experience in ML Engineering within cloud-native environments
• In-depth knowledge of Python (required); familiarity with JavaScript/TypeScript is a nice-to-have
• Strong experience with infrastructure and DevOps tools including Kubernetes, Docker, and Ansible
• Proven expertise with cloud platforms (AWS preferred, plus GCP and Azure)
• Experience architecting distributed systems, storage systems, and databases
• Hands-on experience with machine learning frameworks such as PyTorch and LangGraph
• Experience with orchestration tools like Airflow (preferred) or similar
• Proficiency with infrastructure-as-code tools (CDK, Terraform, Pulumi, CloudFormation, etc.)
• Experience with monitoring, tracing, and logging tools (CloudWatch, NewRelic, Grafana, etc.)
• Excellent communication skills, strong ownership mindset, and systematic problem-solving approach
• Demonstrated ability to lead incident response and conduct blameless postmortems to prevent recurrence

🏖️ Benefits

• Comprehensive medical, dental, vision, and life insurance plans
• HSA with employer match, FSA, and DCFSA options
• 401(k) retirement plan
• 11 paid company holidays
• Remote-first work model with location flexibility
• Flexible PTO policy
• Annual company-wide offsite and periodic team offsites
• Annual equipment stipend for home office setup

Skills & Technologies

Python

JavaScript

TypeScript

PostgreSQL

AWS

DevOps

Remote

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Rad AI

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About Rad AI

Rad AI provides AI-powered radiology workflow software for hospitals and imaging centers. Its platform automates repetitive tasks in radiology reporting, such as follow-up recommendations and incidental findings detection, reducing radiologist burnout and improving patient care. The company’s products integrate with existing PACS and RIS systems to streamline radiology operations. Rad AI serves healthcare systems across the United States.

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