Staff Machine Learning Engineer, Infrastructure
About Rad AI
At Rad AI, we’re on a mission to transform healthcare with artificial intelligence. Founded by a radiologist, our AI-driven solutions are revolutionizing radiology—saving time, reducing burnout, and improving patient care. With one of the largest proprietary radiology report datasets in the world, our AI has helped uncover hundreds of new cancer diagnoses and reduced error rates in tens of millions of radiology reports by nearly 50%.
Rad AI has secured over $140M in funding, including a recently oversubscribed Series C ($68M round) led by Transformation Capital, bringing our valuation to $528M. Our investors include Khosla Ventures, World Innovation Lab, Gradient Ventures, Cone Health Ventures, and others—all backing our mission to empower physicians with cutting-edge AI.
Our latest advancements in generative AI are used by thousands of radiologists daily, supporting more than one-third of radiology groups and healthcare systems and nearly 50% of all medical imaging in the U.S. at partners including Cone Health, Jefferson Einstein Health, Geisinger, Guthrie Healthcare System, and Henry Ford Health.
Recognized as one of the most promising healthcare AI companies by CB Insights and AuntMinnie, and ranked by Deloitte as the 19th fastest-growing company in North America, we are building AI-powered solutions that make a real impact. Most recently, Rad AI was named to CNBC’s Disruptor 50 list, highlighting the innovation and momentum behind our mission.
If you’re ready to shape the future of healthcare, we’d love to have you on our team!
Why Join us?
We’re looking for a Staff Machine Learning Engineer to join our MLOps team and help build and maintain the infrastructure that supports our cutting-edge AI research and products. In this role, you’ll develop tools and systems that accelerate language model R&D and serve those models to radiologists, ultimately improving clinical outcomes for patients.
You’ll play a key role in designing and implementing the infrastructure that connects our models to our customer-facing products. This role is backend-focused and will primarily include development in Python.
This is a unique opportunity to work at the intersection of AI and healthcare, shaping the future of how radiologists care for patients.
What You’ll Be Doing:
Architect the infrastructure that supports our machine learning applications, services, and workflows
Architect and maintain our ML platform that supports continuous integration, continuous delivery, and continuous training for our machine learning models
Develop cloud-native services and serverless architectures to build scalable and resilient systems
Partner with data scientists to design the data pipeline that enable various machine learning models in production
Write code that meets our internal standards for security, style, maintainability, and best practices for a high-scale HIPAA web environment
Design, deploy, and maintain the full ML platform stack including monitoring and observability, data analytics, backend integration with customer-facing products, and the full model R&D lifecycle
Work with Product Management, Research, and Engineering to iterate on new features and address inefficiencies across our AI/ML infrastructure
Who We’re Looking For:
8+ years of industry experience in ML Engineering in cloud-native environments
In-depth knowledge of Python (required), Javascript/Typescript (nice to have), or other modern languages in the ML domain
Strong experience with infrastructure and DevOps tools such as Kubernetes, Docker, and Ansible
Strong knowledge of cloud computing platforms such as AWS (preferable), GCP, and Azure
Experience architecting distributed systems, storage systems, and databases
Experience working with machine learning frameworks such as PyTorch and LangGraph
Experience with Airflow (preferable) or other orchestration tools
Experience with infrastructure-as-code tools such as Terraform (preferable), Pulumi, Cloud Formation, etc.
Experience with monitoring, tracing, and logging tools such Cloudwatch, NewRelic, Grafana, etc.
Excellent communication skills, with a strong sense of ownership and a systematic approach to problem-solving
Proven ability to manage and lead active incidents, address what caused them, and establish systems to avoid them in the future via blameless postmortems
Nice To Haves:
Experience working with productionizing or optimizing inference of LLMs or other NLP models
Experience with the Ray ecosystem
Experience with PostgreSQL
Experience with data analytics tools like Hex, Amplitude, Retool, etc.
Experience working at a fast-growing startup
Experience in a HIPAA-compliant environment
We welcome applicants from across the United States, with a strong preference for this role to be based in our San Francisco office.
Join our world-class team as we build and deploy AI solutions that empower physicians and transform patient care—making a meaningful impact on millions of lives. Driven by our mission, we prioritize transparency, inclusion, and close collaboration, bringing together exceptional people to revolutionize healthcare. If you're passionate about driving innovation and delivering impactful healthcare solutions, we'd love to hear from you!
To learn more about what it's like to work at Rad AI, visit https://www.radai.com/life-at-rad-ai
For US-Based Full-Time Roles, Rad AI offers a variety of benefits, including:
Comprehensive Medical, Dental, Vision & Life insurance
HSA (with employer match), FSA, & DCFSA
401(k)
11 Paid Company Holidays
Location Flexibility (Remote-first company!)
Flexible PTO policy
Annual company-wide offsite
Periodic team offsites
Annual equipment stipend
For roles based outside the US, your recruiter can share more details
At Rad AI, we value diversity and provide equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.
Please be vigilant regarding job scams. We advise all candidates to apply directly through our official careers page. Our recruiters will use email addresses with the domain @radai.com or no-reply@ashbyhq.com.