Founding Engineer, Applied AI Lead [Remote]

About Equi

Equi is an investment management firm building institutional-quality alternative investment solutions. We operate a unique business model: in addition to offering Equi-branded funds, we build white-label fund-of-funds products for some of the top independent multi-family offices and financial advisory firms in the country. We combine deep manager-selection expertise with sophisticated portfolio construction to deliver differentiated outcomes for our clients and partners.

The firm is at an inflection point. Our AUM is scaling rapidly, we've just launched our largest client since inception (a 9-figure opportunity) and have ~6 more clients that have signed or at the finish line. We're assembling a small, high-caliber team that can grow with the platform and take on increasing responsibility as we scale.

About the Role

Equi is seeking a Founding Engineer and Head of Applied AI to serve as the lead builder across the entire company. This is a foundational leadership role: you'll design and build the systems that let a small team run an investment firm at much larger scale, working across the investment team, operations and go-to-market.

Equi is designed to run with AI agents doing much of the work and people steering them. Several roles here, from fund finance to go-to-market, are already built around agents. You'll build the systems underneath: pipelines that turn messy investment data into something reliable, agents that read and reason over investment data, carry out multi-step workflows across the investment process, and power new products for our clients. You'll also own what makes those agents trustworthy: the evals, traces and routing that tell us what's working, what isn't, and which model should handle each job.

The tools in this field change every few months. We care less about any particular technique and more about your judgment in choosing what to build with, and your discipline in measuring whether it works.

This is a rare opportunity for someone fascinated by sophisticated investment strategies who wants to build the technology behind a rapidly scaling platform. The role will grow from hands-on building to leading a small engineering team, while keeping our vision of a small, flat organization where everyone stays close to the work.

How We Build

These are the principles this role will put into practice from day one.

• Agents do the work; people make the calls that matter. Every workflow has a clear point where a person reviews, approves or overrides.

• If we can't measure it, we don't ship it. Each agent gets an eval suite built from real tasks, and every change to a prompt, tool or model runs against it before reaching production.

• Every model call and tool call is traced. When something goes wrong we can replay it, and production traces become new eval cases.

• We're model-agnostic. Each task goes to the model that fits it on quality, cost and latency, and we switch when the evals show something better has shipped.

• Engineers work alongside coding agents. You'll spend your time on architecture, review and judgment calls, and set up the codebase so agents can work in it safely.

• Built for a regulated firm. We're an SEC-registered investment adviser, so every agent action is logged, attributable and reviewable by design.

Culture

Equi is a team-first environment where transparency and healthy debate are integral to our success. We operate with a flat structure: ideas win on merit, not seniority, and every team member is expected to contribute to the conversation. We value intellectual honesty, rigorous thinking, and the humility to change your mind when the evidence warrants it.

Our vision for engineering is to keep the team small and flat, with everyone remaining involved in building rather than adding layers of management. As the platform scales, there is a clear path to broader responsibility and leadership while staying deeply connected to the technical work.

What You'll Do

Lead Builder Across the Company

• Serve as the primary engineer and technical leader, building software that accelerates growth across all teams

• Partner directly with the Investment Team, Operations and GTM to find the workflows where agents can take on real work

• Own complex projects end to end, from first prototype through production and ongoing improvement

• Make the key architecture decisions for a growing platform

• Track new models and tools as they ship, test which ones are worth adopting, and bring the rest of the team along

Agent Systems

• Build agents that take in investment data in any format and turn it into structured data that people and systems can use

• Build agents that run multi-step workflows across the investment process, with clear points where people review and approve

• Give the investment team analysis and recommendations they can check, with a source for every claim

• Design the tools, permissions and context each agent gets, and decide what each one must never be allowed to do

• Build in a full audit trail of what each agent did and why

Data Engineering

• Build and own the pipelines that bring in data from fund managers, administrators and internal systems

• Clean, reconcile and normalize messy data (inconsistent formats, missing fields, conflicting sources) into a single source of truth

• Design the data models that the investment team, agents and client products all rely on

• Put data quality checks and alerts in place so bad data is caught before it reaches a report or an agent

Evals, Tracing & Model Routing

• Build eval suites from real tasks and real failures, and run them on every change to a prompt, tool or model

• Calibrate automated graders against human review so the scores mean something

• Capture traces of every model and tool call, with cost and latency, and use them to debug, replay and grow the eval set

• Route each task to the right model on quality, cost and latency, with fallbacks when a provider goes down

• Test new models against our evals as they ship, and switch when the results justify it

• Track cost per workflow and keep spend in line with the value each agent delivers

Customer-Facing Applications

• Turn complex user needs into well-designed, maintainable full-stack applications

• Design and run reliable, performant APIs and backend services

• Build experiences that make agent output useful and trustworthy for clients and partners

Team Leadership & Growth

• Set technical direction and engineering practices, including how the team builds with coding agents

• Lead architecture discussions and technical decisions

• Push for better processes and new tools where they pay off

• Eventually hire and lead the engineering team while staying hands-on

Who You Are

We're looking for a builder with technical depth, product judgment and leadership instincts, who measures everything they ship.

Required

• Experience: 5+ years of professional software development, with growing responsibility and impact

• Passion for Investing: Experience can't substitute for an innate curiosity and passion for the world of investing. We operate in the depths of it, so you better love it!

• Agents in Production: You've shipped agent systems that real users depend on, not just demos, and you've handled what breaks once they're live

• Evals & Tracing: You've built your own eval suites, not just a vendor dashboard, and instrumented systems so every model and tool call can be inspected, replayed and tied to cost and latency. You can explain how you knew your evals measured the right thing.

• Model Routing: You've routed work across multiple models or providers and made quality, cost and latency trade-offs with data

• AI-Native Engineering: You build with coding agents every day and know how to set up a codebase, tests and CI so agents can work in it safely

• Full-Stack Proficiency: Proven track record of designing, building, deploying and operating complex full-stack web applications in production, with the range to choose the right languages and tools for each job

• Data Engineering: Hands-on experience cleaning, reconciling and modeling messy real-world data, and building the ETL/ELT pipelines and warehouses that keep it reliable. You've owned data quality, not just consumed clean tables

• Product Acumen: Demonstrated ability to think critically about product strategy, understand user needs and pain points, and translate them into technical solutions

• Autonomy & Action Orientation: Proven ability to work independently, navigate ambiguity, and drive projects to completion with a strong sense of ownership and a bias toward action

• Collaboration & Communication: Excellent verbal and written communication, with the ability to explain complex technical ideas clearly to technical and non-technical audiences

• Leadership Potential: Desire and ability to eventually grow and lead a small, high-performing engineering team

Preferred

• Prior experience in fintech, investment technology or financial services, ideally somewhere with audit or compliance requirements

• Experience extracting structured data from messy documents at scale (PDFs, spreadsheets, scanned statements)

• Experience fine-tuning, distilling or self-hosting open-weight models where that beats calling a frontier API

• Experience designing and scaling large-volume data pipelines or distributed systems in the cloud

• Familiarity with advanced data modeling techniques

• You follow AI research closely enough to put new results to work quickly

Defining Our Ideal Candidate: Key Traits in Action

Beyond technical skills, we seek engineers who embody specific traits that are crucial to our culture and success. These qualities are interconnected and define how we approach building impactful products.

Product-Minded

We value engineers who look beyond the code to understand the bigger picture. A product-minded engineer at Equi is deeply curious about the "why": Why are we building this feature? Who is it for? What problem does it solve? They actively engage with stakeholders across the company, offering thoughtful suggestions, constructively challenging assumptions, and proposing alternative approaches based on their technical insight and understanding of user needs. They excel at evaluating the trade-offs between engineering effort and product impact, often leveraging data to support their perspectives and ensuring that we build not just functional solutions, but truly valuable ones.

High Autonomy

Trust is fundamental to our engineering culture, and we empower our engineers with significant autonomy. High autonomy at Equi means having the freedom to determine the best way to tackle complex problems and execute tasks within the scope of agreed-upon goals. Engineers who thrive here are self-starters, comfortable navigating ambiguity, proactively seeking information, and taking ownership of their work end-to-end. This freedom comes paired with accountability; our engineers own their decisions and outcomes, learning and adapting from both successes and setbacks in a psychologically safe environment.

Bias Toward Action

In the fast-paced world of fintech, speed and decisiveness matter. A bias toward action at Equi means valuing progress and learning through doing rather than getting stalled by over-analysis or the pursuit of unattainable perfection. Engineers with this trait are adept at making calculated decisions, even with incomplete information, understanding that many choices are reversible and that iteration leads to better results. They proactively identify and remove roadblocks, break down large problems into manageable steps, readily experiment, and use feedback to adjust course quickly, ensuring that we continuously deliver value to our customers. This proactive approach, enabled by autonomy and guided by a product mindset, is key to our innovation cycle.