AI Engineer

• Build 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.

Do you have experience in TypeScript?, * 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

4.04.0 out of 5 stars San Francisco, CA Hybrid work $180,000 - $250,000 a year - Full-time, * Base salary: US$180,000 to US$250,000

• ESOP: Available
• US$1,000 per month food and commuting allowance
• Laptop of choice

Compensation Range: $180K - $250K