Research Engineer

Fluency is enabling the autonomous Enterprise. (in person)

You're needed to push the boundaries of what our models can understand. We're not prompt engineering chatbots. We're building evaluation frameworks and research systems that measure, improve, and validate enterprise intelligence at a scale nobody has attempted.

Fluency is looking for a Research Engineer to design experiments, build evaluation infrastructure, and drive model quality for our process conformance, productivity measurement, and AI impact analysis across Fortune 500 organisations.

The Problem Space

You'll be developing the methodology and systems that determine whether our models actually work. Screenshots, OCR text, application metadata, behavioral signals: the inputs are messy and the ground truth is ambiguous. The challenge is building rigorous evaluation frameworks that quantify model performance and identify improvement opportunities.

This means:

* Designing evaluation pipelines that measure accuracy, precision, and recall across classification tasks

* Building ground truth datasets from ambiguous, real-world enterprise data

* Running systematic prompt engineering experiments to optimize LLM performance

* Developing A/B testing infrastructure for model comparison

* Researching novel approaches to process understanding, activity classification, and intent extraction

* Quantifying cost-accuracy tradeoffs across different model architectures and prompting strategies

* Building automated world-model training infrastructure from our ontology

The playbook doesn't exist. You'll write it.

We're backed by T1 VCs like Accel, research labs like from Princeton, 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 LLM evaluation, experimental design, and applied research.

About The Role

We're looking for someone with:

* Strong Python fundamentals and software engineering discipline

* LLM prompt engineering and optimization (token efficiency, few-shot design, chain-of-thought)

* Experience evaluating model performance: accuracy measurement, error analysis, regression detection

* Ability to read, synthesize, and apply ML research papers

* Statistical literacy: understanding when results are meaningful vs noise

* 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 founder has a 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

Strongly Preferred

* Experience building evaluation frameworks and benchmarking systems

* Ground truth dataset creation and annotation pipeline experience

* Experience with hybrid LLM/rule-based systems

* OCR, document understanding, or computer vision background

* Cost optimization for LLM-heavy systems

* Classification and NLP systems experience

* Published research or formal research methodology training

* Familiarity with process mining or workflow analysis

* Interesting personal projects that demonstrate depth

Our Customers

We work with some of the world's largest:

* Financial services enterprises (Aon)

* Manufacturing enterprises (Misumi)

* And many more across the enterprise spectrum (PVH)

Our Culture

You're expected to be in love with the craft. You're expected to like laughing. You're expected to want to work on novel problems. You're expected to find satisfaction in novelty. You're expected to solve under obscurity.

Our Values

* In hesitation lies destruction; in action, glory.

* Those who merely meet expectations abandon the pursuit of greatness.

* One who dwells within the forum must regard it as hallowed ground.

* One who has not tasted the grapes declares them sour.

* One who sits alone at the feast misses the richness of the table.

Location

Full-time, in-person role based in San Francisco, CA.

* We offer E3 sponsorship for Australians to relocate with stipend

Compensation

* US$150K - $320K salary, depending on candidate and experience

* Substantial equity, every offer includes ownership

* Mac, Linux, or Windows, your call

* High-impact work with global enterprises

* Technical, product-led founders

Don't apply if:

* You want hybrid or remote

* You don't like working hard and with insane velocity

* You want to work a 9 to 5

* You're not comfortable with rapid iteration

* You think evaluation is grunt work

* You've never shipped a model or evaluation system to production

* You don't have personal projects

* You dislike constraints (we have them: cost, latency, accuracy tradeoffs are real)

* You aren't ambitious

* You don't have a good reason for wanting to work at an early-stage company

Hiring Process

* Resume screen

* 1:1 with founder

* Technical deep-dive on past research and evaluation work

* Work through a real problem with the team - usually as a live coding exercise

* Offer

We strongly encourage applicants from underrepresented backgrounds to apply. Diverse teams build better products, see value #5.

Compensation Range: $150K - $320K