AI Engineer

AI Engineer

Location: San Francisco, CA or Phoenix, AZ (In-Office)

Partnership: EQL Tech has been exclusively retained by a high-growth technology startup to appoint a mission-critical AI Engineer to own the brand feel of the company and the movement they're building.

About the Company & The Mission

EQL Tech is proud to represent a highly ambitious, well-funded startup that has raised $16M from top-tier VCs and angels. The company is building the financial rails to help families access new State education funds (known as ESAs or School Choice Funds).

The $900B US Public Education budget is being opened up for parents to take control of their portion, which averages $7.5k per kid per year. Ambitious homeschool parents are already using these funds to piece together their dream education experience. Helping them access these funds is Step 1 in the journey to build the next-gen education system. The company treats this as their life's work and has already rejected an acquisition offer because they care about this being done right.

The Team

• The Founders: The founders are engineers who have run their own alternative school together. One previously worked as a Quant at Goldman Sachs, and the other built the computer vision system for the largest smart warehousing company globally, serving 1M customers/day at age 19.
• The Core Team: You will work alongside top talent, including a founding engineer who did AI research at MILA and at Elon Musk’s SpaceX school, a Head of Risk from Mercury, Stripe, and Circle, and a Payments Engineer from Microsoft and Goldman Sachs. The team also includes the former Deputy Director at Arizona's ESA department and leading school choice advocates.

The Role: AI Engineer

As AI Engineer, you will work directly under the Head of AI — a researcher with experience at one of the world's leading ML research labs — to build and ship the intelligence layer that powers the product. AI is not a feature here; it is the core of how families get instant eligibility decisions, and how the company scales compliance without scaling headcount. You will own AI products end-to-end, from first prototype to production.

As AI Engineer, you will

• Build MVPs from scratch: take new AI products from zero to real users — both consumer-facing and internal tooling — with minimal hand-holding and a high bar for quality
• Optimise accuracy and latency: tune LLM and VLM pipelines, and classical ML models where appropriate, to meet the standards a regulated fintech product demands
• Create robust evals: build evaluation frameworks that make AI behaviour measurable, reproducible, and improvable over time — so regressions are caught before users feel them
• Read data and fix mistakes: diagnose real-world AI failures by going directly into the data, making ad-hoc corrections, and closing the loop fast
• Build endpoints and tooling: surface AI capabilities to teammates in reliable, well-documented ways so the whole team can move faster without depending on you for every query
• Work across the full AI stack: primarily LLM and VLM-based in early stages, with scope to fine-tune or train models from scratch as individual products mature and optimisation demands it

Your Profile

• Biased toward simplicity: you know that managing many AIs gets complex fast — you resist unnecessary abstraction and build systems that are easy to reason about and maintain
• Values old and new AI equally: you recognise the tradeoffs between prompting, fine-tuning, and training from scratch — and you pick the right tool for the job rather than defaulting to the latest trend
• User-obsessed: AI is the blocker to a good number of AHA moments in the product — you keep the end-user in mind in every technical decision, not just the benchmark
• No task beneath you: reading data, making database edits to correct AI mistakes, writing evals for edge cases — you treat this as essential product work, not a distraction from "real" engineering
• Comfortable with ambiguity: you can scope your own work, define your own quality bar, and ship without waiting to be unblocked
• Able to work in-person with the team in San Francisco, CA or Phoenix, AZ (visa support available)
• Experience with LLM APIs, vector databases, fine-tuning pipelines, or evaluation frameworks is a strong plus

Commitment, Compensation & Benefits

• Competitive Salary: $125,000 – $175,000 per year, commensurate with experience.
• Generous Founding Equity: We compensate you well with equity.
• Top-Tier Backing: We've raised $16M from top-tier VCs and angels.
• Relocation Support: You are willing to relocate to San Francisco, CA, or Phoenix, AZ, and travel to visit customers. We're an in-person company and are in the office together.
• Comprehensive Visa Sponsorship: If you do not have a visa, we can support you.
• Unmatched Impact: The rare opportunity to directly shape how the $900B US Public Education budget is being opened up for parents to take control of their portion (avg. $7.5k/kid/year).