As an AI Infrastructure / Product Engineer, you will work at the intersection of AI research and product development at a healthtech company building AI products. You will help turn experiments into dependable systems and features, and build ways to measure and improve their quality.
Build repeatable benchmarks and evaluation pipelines to measure model accuracy, hallucinations, retrieval quality, latency, and cost.
Create dashboards that make changes in system performance visible to the team.
Develop production retrieval, drafting, document, and agent workflows from research prototypes.
Build backend services, APIs, and internal tools to help test and release improvements.
Deploy and operate cloud infrastructure, including monitoring, databases, delivery pipelines, and reliable operations.
Collaborate with AI research while staying connected to product and user needs.
At least 3 years of experience as a software, infrastructure, or product engineer building and deploying production systems.
Strong Python skills and hands-on cloud deployment experience.
Experience with APIs, databases, backend services, deployment pipelines, and production monitoring.
Experience building evaluation pipelines, benchmarks, or assessment systems for machine learning or AI systems.
Ability to turn ambiguous ideas and experimental code into usable, maintainable products.
Interest or experience in LLMs, retrieval-augmented generation, agents, AI benchmarks, or document automation.
Full-stack experience is helpful. Familiarity with FastAPI, Docker, React, Next.js, Postgres, or vector databases is a plus.
Annual base salary is $180,000 to $260,000 USD, depending on skills, experience, and role scope. Visa sponsorship is not available.
Remote within the United States.
Design and deploy AI and Generative AI solutions, including LLM-powered applications, RAG pipelines, and agent-based automation, taking projects from rapid prototypes to production systems.
Design and own the evaluation, testing, and observability infrastructure for AI agents, building automated pipelines and statistical, regression, and adversarial tests to improve reliability, safety, and performance.
Design and own evaluation, testing, and observability infrastructure for AI agents, building automated pipelines and statistical and adversarial tests to improve reliability, safety, and performance.
Design and own the evaluation, testing, and observability infrastructure for AI agents, building automated pipelines and statistical, adversarial, and regression tests to improve reliability, safety, and performance.
Design and own the evaluation, testing, and observability infrastructure for AI agents, building automated pipelines and statistical, adversarial, and multi-turn tests to improve reliability, safety, and performance.
