AI Engineer

Braintrust•Seattle, WA

30d

About The Position

We’re looking for an AI Engineer to work directly with our most strategic customers and help them successfully deploy, scale, and extract value from Braintrust in real production environments. This is a deeply technical, customer-facing role at the intersection of engineering, product, and go-to-market. You’ll partner closely with customer engineering teams to instrument real AI workflows, establish production baselines, operationalize evaluations, and build the feedback loops that make AI systems reliable at scale. You will need strong judgment about how AI systems behave in the real world, how to evaluate them, and how to improve them iteration by iteration. If you are excited by the challenge of solving open-ended problems, enjoy shipping quickly, and take full ownership of customer outcomes, this role offers outsized impact on both customer success and Braintrust’s product roadmap.

Requirements

3–7+ years of experience as a software engineer or forward-deployed / field engineer
Strong backend or full-stack engineering skills (Python strongly preferred; TypeScript a plus)
Hands-on experience working with LLMs, APIs, or agentic workflows in production environments
Familiarity with cloud infrastructure and deployment patterns (AWS preferred; Docker/Kubernetes a plus)
Comfortable working directly with customers and owning technical outcomes end-to-end
Strong communication skills and ability to translate between business needs and technical implementation
Bias toward action: you enjoy shipping scrappy but production-ready solutions and iterating quickly

Nice To Haves

Experience with AI observability, evaluation frameworks, or ML/LLMOps tooling
Prior experience in a startup, founding team, or 0→1 product environment
Experience supporting enterprise or self-hosted deployments
Willingness to travel occasionally for on-site customer engagements

Responsibilities

Partner closely with customer engineering teams to deploy, stabilize, and continuously improve AI applications in production
Instrument and trace real-world AI workflows end-to-end, establishing baseline targets for latency, cost, quality, and reliability
Turn production data into datasets and evaluations; define scoring rubrics and implement CI quality gates
Build prototypes, integrations, and custom workflows that help customers operationalize evaluations and observability as part of their SDLC
Deploy and troubleshoot Braintrust in customer environments (cloud or self-hosted), working across application, data, and infrastructure layers
Act as the technical lead in customer engagements, running an operating cadence and feeding real-world learnings back into Product and Engineering