AI Technical Lead (Principal Engineer)

EverQuote•Cambridge, MA

2d•Hybrid

About The Position

EverQuote is a leading AI-powered growth solutions partner for regulated property and casualty insurance entities, enabling the largest insurance carriers and thousands of agents to maximize customer acquisition across digital channels. Fueled by our proprietary data assets and our AI traffic engine, EverQuote is transforming the way providers attract and engage consumers to grow market share. Joining our team puts you at the forefront of this transformation. You will collaborate with a group of builders to solve complex problems and reshape a massive industry. If you want to drive real-world impact and do the best work of your career, you’ve found your home. We move fast, follow the data, and hold ourselves to high standards. If that's how you work too, this is the role for you. As our Technical Lead, you will design and build a new kind of product: an insurance marketplace that AI agents use to shop on a person’s behalf. You will own the technical architecture end to end, from the models and how they run to the MCP server and tools that other AI platforms connect to. This is a hands-on senior role on a small team, and the decisions you make early will shape what agentic insurance shopping looks like before the market arrives. You own the technical architecture for the agent: where the model’s reasoning runs, how we tune and serve it, how we keep cost and speed under control, and how the whole system stays reliable. We intend to run and tune our own open-weight models rather than depend only on hosted APIs, so we control cost, speed, and behavior as volume grows, and owning that is central to this role. You own the MCP server, the tools, and the data the agent works with, and you set the patterns the rest of the team builds on. You are the person who decides, with testing to back it up, how this is built, and then builds it.

Requirements

Deep, current, hands-on experience in the AI and LLM space. You work on this every day, not from the sidelines.
Real experience with open-weight models: you have fine-tuned them (LoRA, QLoRA, SFT, DPO, or similar) and run them yourself, not just called a hosted API.
Experience owning an LLM system in production: you have served models reliably, pinned versions, rolled back a bad change, and explained why the bill moved.
Enterprise-level design and architecture experience, with a track record of leading large, ambitious projects and delivering them.
Strong grasp of evaluation and guardrails: building eval sets, catching failure modes, and monitoring quality, drift, cost, and speed.
Fluency with the serving and ops stack: model serving, containers and orchestration, and monitoring, on at least one major cloud.
Strong communication. You can explain a technical trade-off to an engineer and to a non-technical leader, and lead with data.
Comfort with ambiguity and a bias for action in a space where the standards change month to month.
8+ years in engineering, with senior or lead ownership of complex technical systems, including hands-on work in machine learning or LLMs.
A track record of leading large, ambitious projects with a small team and delivering them. We care more about what you have shipped and the judgment you showed than the revenue the project generated.
Hands-on fine-tuning and deployment of open-weight or open-source models, with real production experience.
Enterprise design and architecture experience for complex, high-reliability systems.
Strong Python, Rust, and fluency with the modern LLM stack (serving, retrieval, evaluation, monitoring).
Strong strategic thinking paired with methodical execution. You can set the technical direction and then deliver it.
Excellent communication across all levels of an organization, technical and non-technical.
A history of teamwork and a bias for action.

Responsibilities

Own the architecture: decide where the agent’s reasoning runs, which models we use, how they are packaged, and how AI platforms connect to us, and back those calls with testing.
Build and tune the models: fine-tune open-weight models for our use, and decide when tuning, retrieval, or prompting is the right tool.
Stand up the serving and infrastructure: deploy the agent and MCP server so they are reliable, fast, and affordable to run, with versioning and a way to roll back.
Build the testing into the system: programmatic eval sets, guardrails, and monitoring for quality, drift, cost, and speed, so we catch problems before agents do.
Design the data model and tools with the AI product lead: the request and response payloads, the reasoning we expose, and the trust signals.
Set the engineering patterns and standards the team builds on, and keep the code and architecture documented.
Keep cost and speed honest: watch what inference costs, and make the trade-offs that keep it sustainable as volume grows.
Work in the open. Help publish our code, architecture, and findings.
Mentor the engineers on the team and give leadership clear, regular visibility into technical progress and risks.