AI Engineer

Job Description

Job Description

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.

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.

You will be joining an in-person company, working together in the office.

• 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.

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

Requirements

• 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

Benefits

This will be a big commitment, and we're aiming high. It needs to be something you are energised about taking on, or this isn't the team for you.

• 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).