AI Technical LeadLocation: Cambridge, MA / Hybrid
(This role requires working in-office 3 days per week: Tuesday, Wednesday, and Thursday.)
About the role: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.
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.
What You Bring:- 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.
Qualifications: - 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.
The base salary offered for this position is determined by several factors, including the applicant's job-related skills, relevant experience, education or training, and work location. The base salary for this full-time hybrid role is $202,800-$238,600.
In addition to base salary, our total compensation package may include participation in variable cash bonus programs, equity, and a comprehensive range of health, welfare, and wellbeing benefits based on eligibility which are designated by role.
Your recruiter can provide more details about additional compensation components, such as OTE structure, bonus targets, or equity, during the hiring process.
This role is not eligible for immigration sponsorship, now or in the future. Candidates must have current, unrestricted work authorization in the United States.
