ML/AI Engineer

Qualifications

• You're needed to build the intelligence layer that understands how work actually happens
• 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
• Helping with product thinking
• You're expected to like laughing
• You're expected to want to work on novel problems
• You're expected to solve under obscurity
• One who dwells within the forum must regard it as hallowed ground
• 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 prompt engineering is beneath you
• You've never shipped a model to production
• You don't have personal projects
• Technical deep-dive on past ML work

Benefits

• We offer E3 sponsorship for Australians to relocate with stipend
• Compensation
• US$150K - $250K salary, depending on candidate and experience
• Substantial equity - every offer includes ownership
• Mac, Linux, or Windows - your call

Responsibilities

• Fluency is enabling the autonomous Enterprise
• 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
• 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
• There will be an expectation to stay up to business context, which could involve:
• Watching key customer calls
• Interacting with customers
• You're expected to be in love with the craft

Full Description

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

Strongly Preferred

• 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

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 - $250K 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 prompt engineering is beneath you

• You've never shipped a model to production

• You don't have personal projects

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

• You aren't ambitious

Hiring Process

• Resume screen

• 1:1 with founder

• Technical deep-dive on past ML work

• Work through a real problem with the team

• Offer

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