ML/AI Engineer

Fluency•San Francisco, CA

23h•$150,000 - $250,000•Onsite

About The Position

Fluency is enabling the autonomous Enterprise. You're needed to build the intelligence layer that understands how work actually happens. We're not fine-tuning chatbots. We're building systems that comprehend, classify, and quantify enterprise workflows at a scale nobody has attempted. Fluency is looking for an ML/AI Engineer to design and build the models that power process conformance, productivity measurement, and AI impact analysis across Fortune 500 organisations. The Problem Space You'll be building hybrid ML systems that operate on messy, real-world data: screenshots, OCR text, application metadata, and behavioural signals. The challenge is extracting structured understanding from unstructured chaos, at scale, with cost constraints that make brute-force LLM calls untenable. This means: Designing classification systems that detect AI tool usage across thousands of applications Building process conformance models that compare observed workflows against ideal templates Creating attribution models that quantify productivity impact with statistical rigour Optimising inference pipelines to balance accuracy against token economics The playbook doesn't exist. You'll write it. We're backed by T1 VCs like Accel and are hitting an inflection point with Enterprises all around the globe. You'll work directly with founders and our engineering team on technical challenges that span classical ML, LLM orchestration, and production systems engineering. About the Role We're looking for someone with: Strong Python fundamentals and software engineering discipline Experience building classification and NLP systems LLM prompt engineering and optimisation (token efficiency, few-shot design, chain-of-thought) Evaluation methodology: building ground truth datasets, A/B testing, accuracy measurement Production ML experience: model serving, latency optimisation, monitoring Comfort with ambiguity and novel problem domains Computer Science Background - with caveat. If you don't have a CS background, you're challenged to beat one of the founders in a 1:1 whiteboard duel on DS&A judged by Hung. Neither founders have formal CS background, but come prepped. There will be an expectation to stay up to business context, which could involve: Watching key customer calls Interacting with customers Helping with product thinking

Requirements

Strong Python fundamentals and software engineering discipline
Experience building classification and NLP systems
LLM prompt engineering and optimisation (token efficiency, few-shot design, chain-of-thought)
Evaluation methodology: building ground truth datasets, A/B testing, accuracy measurement
Production ML experience: model serving, latency optimisation, monitoring
Comfort with ambiguity and novel problem domains
Computer Science Background - with caveat. If you don't have a CS background, you're challenged to beat one of the founders in a 1:1 whiteboard duel on DS&A judged by Hung. Neither founders have formal CS background, but come prepped
There will be an expectation to stay up to business context, which could involve: Watching key customer calls Interacting with customers Helping with product thinking

Nice To Haves

Experience with hybrid ML/rule-based systems
OCR, document understanding, or computer vision background
Cost optimisation for LLM-heavy systems
PyTorch or similar framework experience
Familiarity with process mining or workflow analysis
You've shipped ML systems that operate at scale under real constraints
Interesting personal projects that demonstrate depth

Responsibilities

Designing classification systems that detect AI tool usage across thousands of applications
Building process conformance models that compare observed workflows against ideal templates
Creating attribution models that quantify productivity impact with statistical rigour
Optimising inference pipelines to balance accuracy against token economics