AI Engineer

We’re hiring a full-time AI Engineer to own the prompts, agents, evals, and pipelines behind user-facing features that ship to users. You’ll take product requirements and turn them into working prompts, agents, and pipelines. You’ll evaluate them rigorously, iterate until they’re production-ready, and keep improving them once they ship. This role sits at the intersection of product and platform: you decide what the AI should do, prove it works, and get it in front of users. Because we’re an early-stage company moving fast, we’re looking for someone who can work quickly through ambiguous AI problems, measure output quality, and ship only when the system is reliable enough for production. This is an in-person role, 5 days a week in our office. The ability to tell the difference between “looks good in the demo” and “works in production” is essential. Key ResponsibilitiesBuild new AI features end to end, from prototype to production.Improve AI output quality through prompt engineering, model selection, retrieval, and evaluation.Design and run evals that measure real output quality, not just first impressions.Iterate fast on prompts, agent designs, and orchestration patterns.Partner with the Product Engineer to translate requirements into AI features that actually work.Partner with the AI Platform team to land features on solid infrastructure.Evaluate new models, tools, and techniques when they improve quality, latency, cost, or reliability. What We Are Looking ForHands-on experience building LLM-powered features that shipped to real usersProduction engineering chops in TypeScript/Node (primary, especially in AWS Lambda) and/or PythonExperience with multiple LLM providers such as Anthropic, OpenAI, Google Vertex, AWS Bedrock, or similarPractical judgment in prompt engineering, retrieval, and agent design, backed by evaluation resultsTrack record of building evaluation systems that actually catch regressionsSolid software engineering fundamentals: you can write production code, not just notebooks Nice to HaveExperience with provider-abstraction libraries for multi-LLM workflowsFamiliarity with pgvector or other vector retrieval systemsExperience with post-training or fine-tuningExperience deploying AI features on AWS Lambda, ECS Fargate, or similarBackground in ML, NLP, or applied researchExperience with structured output, function calling, and tool use at scaleExperience with Anyscale Ray or similar distributed compute frameworks for batch inference, eval pipelines, or scaling agent workloadsOpen source contributions in the LLM or agent tooling space About FluencyFluency builds a platform that captures how work actually happens inside large organizations, measures productivity and process conformance, and analyzes where AI can do the work. We capture observable work data across tools and systems, structure it into a model of how work runs, and use it to measure productivity, check process conformance, and analyze where AI changes the work. Fluency is looking for an AI Engineer to own the AI quality and the prompts, agents, evals, and pipelines behind user-facing features that ship to Fortune 500 users. Our CustomersCustomers include CVS Health, Aon, and PVH. LocationFull-time, in-person role based in San Francisco, CA.We offer E-3 sponsorship for Australians to relocate with stipend. This role is not a fit ifYou want hybrid or remoteYou’re not comfortable with rapid iterationYou’ve never operated production pipelinesYou dislike constraints (we have them: cost, latency, reliability tradeoffs are real)You don’t have a good reason for wanting to work at an early-stage company Hiring ProcessResume screen1:1 with founderTechnical deep-dive on past AI engineering workWork through a real problem with the teamOffer We strongly encourage applicants from underrepresented backgrounds to apply. Diverse teams build better products. Compensation & BenefitsBase salary: US$180,000 to US$250,000ESOP: AvailableUS$1,000 per month food and commuting allowanceLaptop of choice

• Hands-on experience building LLM-powered features that shipped to real users
• Production engineering chops in TypeScript/Node (primary, especially in AWS Lambda) and/or Python
• Experience with multiple LLM providers such as Anthropic, OpenAI, Google Vertex, AWS Bedrock, or similar
• Practical judgment in prompt engineering, retrieval, and agent design, backed by evaluation results
• Track record of building evaluation systems that actually catch regressions
• Solid software engineering fundamentals: you can write production code, not just notebooks

Nice to Have

• Experience with provider-abstraction libraries for multi-LLM workflows
• Familiarity with pgvector or other vector retrieval systems
• Experience with post-training or fine-tuning
• Experience deploying AI features on AWS Lambda, ECS Fargate, or similar
• Background in ML, NLP, or applied research
• Experience with structured output, function calling, and tool use at scale
• Experience with Anyscale Ray or similar distributed compute frameworks for batch inference, eval pipelines, or scaling agent workloads
• Open source contributions in the LLM or agent tooling space

About Fluency

Fluency builds a platform that captures how work actually happens inside large organizations, measures productivity and process conformance, and analyzes where AI can do the work.

We capture observable work data across tools and systems, structure it into a model of how work runs, and use it to measure productivity, check process conformance, and analyze where AI changes the work., * You want hybrid or remote

• You’re not comfortable with rapid iteration
• You’ve never operated production pipelines
• You dislike constraints (we have them: cost, latency, reliability tradeoffs are real)
• You don’t have a good reason for wanting to work at an early-stage company