Machine Learning Engineer

About the Team

Built and based in Austin, Texas, we are a team of “Closers” who are passionate about changing the future of healthcare. With numerous accolades in our trophy case, we have been repeatedly recognized by the Austin Business Journal as a “Best Places to Work” and Built In as a “Best Startups” for our culture and employee-centric programming.
We believe in empowering our teams to do their best work, offer generous employee benefits that include 100% paid medical insurance for employees and their dependents, flexible PTO, paid parental leave, 401k, and ongoing learning and team building opportunities. We believe that diverse teams are stronger teams and understand that building for the future includes embracing and honoring different backgrounds and experiences. Doing so creates the foundation for our ability to build systems that drive unbiased outcomes.

We strive to create a supportive team high in rigor, with a constant improvement mindset. We believe that teams high in trust innovate faster and better, collaborate more effectively, and love their work more. If this sounds like a place where you can see yourself building for a future that transforms healthcare, we’d love for you to join us!


About the Role
ClosedLoop is building Healthy, a generative AI assistant designed to help patients navigate their healthcare with confidence, empathy, and intelligence. We believe the future of engineering is AI-augmented — and we’re hiring engineers who are excited to work that way.

This isn’t a heads-down coding job. Most of your code will be written with the help of AI — and your role will be to architect the system, steer the tools, and ensure quality. You’ll partner with large language models to design, review, and scale ML infrastructure that brings LLM-powered healthcare experiences to life.We’re looking for someone who thinks in systems, uses AI as a force multiplier, and sees code review and orchestration as their highest-leverage activity.


Responsibilities

Design, iterate, and optimize prompts for healthcare-specific use cases, ensuring output quality, safety, and compliance with medical standards
Implement systematic prompt testing, A/B testing frameworks, and performance monitoring to continuously improve AI responses
Participate in ML infrastructure decision-making, including LLM inference pipelines, prompt orchestration, retrieval systems, and evaluation tools
Use AI pair programming tools (like GitHub Copilot, GPT-4o, Cody, etc.) to accelerate implementation — but always drive clarity, structure, and standards
Review AI-generated code for performance, reliability, and maintainability; refactor and guide as needed
Design and implement CI/CD pipelines for prompt versioning, safety checks, evaluation gating, and rollback
Lead experiments that integrate feedback loops, trust metrics, and safety layers into the user experience
Collaborate with cross-functional teams to productize LLM-driven features, with a focus on speed and safety
Think critically about how we can level-up engineering using AI—tools, workflows, mindset

Required Experience:
Has 4+ years of backend or ML infrastructure experience and 2+ years working with ML/LLM systems
Has 2+ years of hands-on experience in prompt creation and optimization, including systematic prompt engineering methodologies
Are fluent in Python and proficient with systems like Google ADK, Graphiti, FastAPI, LangChain, or PineconeHave experience shipping LLM-based features to production users, not just prototyping
Think of AI as a collaborator, not a competitor—you write better code faster with it, but still own the outcomeLove thinking about tooling, evals, and abstractions that scale how teams work with LLMs
Value trust, safety, and observability in systems that interact with real patients and data

The Fine Print:

We have a strong preference for individuals based in our Austin, TX headquarters who are comfortable with our in-office work culture. We offer relocation assistance to new employees relocating to Austin.
At this time, ClosedLoop is unable to provide visa sponsorship. Candidates must be authorized to work in the United States without the need for sponsorship now or in the future.
A few words on our approach to recruiting: We will never ask an applicant for sensitive or personal financial information during any stage of the recruitment process. We advise all applicants seeking employment with ClosedLoop to review available information on recruitment fraud. Anyone who suspects that they have been contacted by someone falsely representing ClosedLoop should email hr@closedloop.ai