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About This Role
AI Technical Lead
Location: Cambridge, MA / Hybrid
(This role requires working in-office 3 days per week: Tuesday, Wednesday, and Thursday.)
About us:
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.
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.
EverQuote is an equal opportunity employer and considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, age, disability, genetic information, pregnancy or a pregnancy-related condition, veteran or military status, or any other protected characteristic. Reasonable accommodations are available for applicants with disabilities and for sincerely held religious beliefs or practices, upon request.
For more information, visit https://investors.everquote.com and follow on LinkedIn.
Salary Context
This $202K-$238K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $177K across 1606 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,253 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At EverQuote, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 13,052 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,382. Disclosed range: $202K to $238K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,382; Director: $272,300; VP: $235,830.
EverQuote AI Hiring
EverQuote has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Boston, MA, US, Cambridge, MA, US. Compensation range: $152K - $238K.
Location Context
AI roles in Boston pay a median of $215,000 across 360 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,253 open positions tracked in our dataset. By seniority: 107 entry-level, 4,462 mid-level, 3,410 senior, and 1,274 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (530 positions). The remaining 8,653 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,253 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,989), AI Software Engineer (462), AI Product Manager (457). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (107) are outnumbered by mid-level (4,462) and senior (3,410) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,274 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (530 positions), with 8,653 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,638 postings), Aws (893 postings), Azure (718 postings), Rag (666 postings), Gcp (532 postings), Pytorch (434 postings), Claude (408 postings), Kubernetes (402 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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