AI Data Engineer

SupportFinity™; San Francisco (CA); On-site; USD 150,000 - 280,000; Full time

Benefits offered by this job

Medical insurance

Vision insurance

Dental insurance

Job summary

Fluency in San Francisco is seeking an AI Data Engineer to design and build data systems that feed process conformance, productivity measurement, and AI impact analysis across Fortune 500 organizations. You’ll handle data from screenshots, OCR text, and behavioral events at scale, building pipelines that are reliable and cost-conscious.

You’ll work directly with founders and engineers, using Dagster/Airflow and Python to ship production data pipelines, feature stores, and serving infrastructure,

Qualifications

• Strong Python fundamentals and software engineering discipline.
• Experience designing production data pipelines (Dagster, Airflow, Prefect, or similar).
• Data modelling for analytical and ML workloads.
• Monitoring and observability for data systems (lineage, quality metrics, alerting).
• Comfort with ambiguity and novel problem domains.

Responsibilities

• Design and build data infrastructure powering conformance, productivity measurement, and AI impact analysis.
• Create feature stores and serving infra balancing freshness against compute cost.
• Orchestrate DAGs coordinating OCR, LLM enrichment, and downstream aggregations.
• Optimize storage and query patterns for time-series behavioral data.

Skills

Python

Production data pipelines

Data modelling

Monitoring & observability

Ambiguity handling

Education

Computer Science background

Tools

Dagster

Airflow

Prefect

dbt

Spark

Job description

Fluency is enabling the autonomous Enterprise. (in person)

You are needed to build the data infrastructure that powers enterprise intelligence. We are not wiring up dashboards. We are building pipelines that ingest, process, and structure the raw signals of how work actually happens, at a scale nobody has attempted. We are the map, the infrastructure, and the action for autonomy to happen.

Fluency is looking for an AI Data Engineer to design and build the data systems that feed our process conformance, productivity measurement, and AI impact analysis across Fortune 500 organizations.

The Problem Space

You’ll be building data infrastructure that handles messy, real-world signals: screenshots, OCR text, application metadata, and behavioral events. The challenge is transforming unstructured chaos into reliable, queryable data that our ML systems can consume, at scale, with cost constraints that make naive approaches untenable.

This means:

• Designing ingestion pipelines that process millions of screenshots and behavioral events daily
• Building data validation and quality systems that catch drift before it corrupts models
• Creating feature stores and serving infrastructure that balance freshness against compute cost
• Optimizing storage and query patterns for time-series behavioral data
• Orchestrating complex DAGs that coordinate OCR, LLM enrichment, and downstream aggregations
• Making sense of work to be ingested by our automation platform

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 data engineering, LLM pipelines, and production systems.

About the Role

We’re looking for someone with:

• Strong Python fundamentals and software engineering discipline
• Experience building production data pipelines (Dagster, Airflow, Prefect, or similar)
• Data modelling expertise: designing schemas for analytical and ML workloads
• Monitoring and observability for data systems: lineage, quality metrics, alerting
• 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 with LLM pipelines and model serving infrastructure
• Shipping models to production: deployment, versioning, monitoring
• OCR, document processing, or image pipeline experience
• Familiarity with dbt, Spark, or similar transformation frameworks
• Experience with multi-region data architectures and residency requirements
• You’ve operated data systems at scale under real constraints
• Interesting personal projects that demonstrate depth

Our Customers

We work with some of the world’s largest:

• Manufacturing enterprises (Misumi)
• Fortune 10 Companies
• 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

• 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 - $280K 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 data engineering is plumbing work
• You’ve never operated production pipelines
• You don’t have personal projects
• You dislike constraints (we have them: cost, latency, reliability 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 data engineering 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.

Medical insurance, Vision insurance, Dental insurance

About the company

Fluency