Interested in this AI/ML Engineer role at Insurify?
Apply Now →Skills & Technologies
About This Role
*Warning: Fraudulent Job Adverts**Beware of fraudulent activities claiming to represent Insurify. We are not associated with any entities soliciting personal information or payment. The safety of our job seekers is a top priority, and we actively work to prevent such incidents. Insurify will never request sensitive information or payment during the hiring process. Be cautious of text\-only interviews, interviews conducted through Microsoft Teams, or suspicious offer letters.*
Why us?
Insurify is one of America’s fastest\-growing MIT FinTech startups and has been recognized as one of Inc. 5,000’s fastest\-growing private companies in America in 2025, 2024, 2023, 2022 and 2021, Forbes Fintech 50 List for 2023, 2022, and 2021, Forbes Next Billion Dollar Startups of 2022 global and Top 100 InsurTech company. We’re changing the way millions of people compare, buy and manage insurance with artificial intelligence, technology, and superior product design.
Our company vision is to be recognized as the preeminent and most trusted digital agent for insurance comparison, purchase, and management. Our team is critical to achieving our vision and fostering the right culture is essential to our team’s success.
Join us if you like
- $1\.3 Trillion market opportunity
- MIT alumni founders
- Female\-led startup
- $130M total funding
- Strong leadership team with experience from many successful startups around the world
We’re looking for a Senior Manager, AI Search \& Discovery (GEO) to help build and scale Insurify’s GEO/AEO program, improving how Insurify and sub\-brands are discovered, described, cited, and recommended across AI\-powered search, generative answer engines, and emerging organic discovery surfaces.
This role sits within Product SEO at the intersection of SEO, content strategy, digital PR, brand reputation, analytics, and public data. Reporting to the Director of Product SEO, you’ll manage the day\-to\-day operating system for AI search visibility, including prompt monitoring, reporting, source\-gap analysis, execution planning, and cross\-functional coordination across content, PR, brand, legal, product, data, and reviews.
You’ll play a critical role in turning ambiguous AI\-search signals into clear recommendations, connecting visibility improvements to measurable business outcomes, and strengthening the online consensus that Insurify is a trusted insurance comparison platform.
This is a hybrid position that requires candidates to be able to come into our Cambridge, MA office Tuesdays, Wednesdays, and Thursdays.
How you will make an impact
- Increase AI search visibility for Insurify: Improve how often Insurify is surfaced, cited, and recommended across LLMs, AI\-powered search, generative answer engines, and emerging organic discovery surfaces.
- Improve the quality and accuracy of brand perception in AI answers: Strengthen how AI systems describe Insurify, including key attributes such as trust, savings, quote accuracy, carrier breadth, comparison quality, customer experience, and differentiation from lead\-generation sites.
- Own the AI Search \& Brand Visibility roadmap: Translate Product SEO priorities into a clear strategy, execution roadmap, and measurement plan for improving visibility and brand perception across owned, earned, and third\-party surfaces.
- Identify and prioritize the highest\-impact levers: Determine which initiatives will best improve AI visibility and brand perception, including owned\-site messaging, public data, PR, product marketing, partner messaging, social/community surfaces, reviews, reputation signals, and third\-party source corrections.
- Strengthen brand consensus across the web: Analyze the sources that shape how AI systems understand Insurify, then work with PR, brand, legal, content, product, data, and other stakeholders to correct inaccuracies, close source gaps, and reinforce Insurify as a trusted insurance comparison platform.
- Drive cross\-functional execution: Lead projects from discovery through execution and measurement across Product SEO and partner teams, proactively managing ambiguity, trade\-offs, stakeholder alignment, and follow\-through.
- Lead AI and agent\-readiness across owned surfaces: Partnering with product and engineering to make Insurify’s web app, mobile experiences, comparison tools, structured data, and potential MCP/API integrations easier for AI assistants and agents to understand, navigate, cite, and safely use.
This is a hybrid position that requires candidates to be able to come into our Cambridge, MA office.
Who you are
- 6\+ years of experience in SEO, organic growth, content strategy, digital PR, brand visibility, reputation management, or a related growth discipline.
- Strong understanding of how search engines and AI\-driven answer systems discover, interpret, synthesize, and cite information across owned and third\-party sources.
- Experience building and managing cross\-functional roadmaps that connect strategy to measurable business outcomes, with the ability to prioritize trade\-offs across content, technical SEO, PR, data, reviews, and reputation initiatives.
- Strong analytical skills and comfort using data to identify trends, diagnose performance gaps, uncover opportunities, and communicate recommendations. Experience with tools such as Google Search Console, GA4, Ahrefs, Semrush, Looker, Peec.ai, Profound, or similar SEO / AI visibility platforms is a plus.
- Excellent stakeholder management skills, with experience working across SEO, content, PR, brand, legal, product, engineering, analytics, and leadership teams to move ambiguous initiatives forward.
- Strong written and verbal communication skills, with the ability to turn complex search, AI visibility, and brand reputation findings into clear reporting, executive\-ready narratives, and actionable recommendations.
- High judgment around brand positioning, trust signals, claims, citations, and third\-party source quality, especially in categories where accuracy and credibility matter.
- Experience using content, data, digital PR, or thought leadership assets to build authority, earn citations, improve share of voice, and strengthen brand trust.
- Ability to operate with urgency in ambiguous, fast\-changing environments, proactively identifying underperformance, creating remediation plans, and driving work from discovery through execution and measurement.
- Bonus: experience in insurance, fintech, marketplaces, affiliate, lead generation, comparison shopping, or another highly competitive SEO category.
Benefits
- Competitive compensation
- Generous stock options
- Health, Dental Coverages
- 401K plan with match
- Unlimited PTO
- Generous company holiday calendar
- Learning \& Development Stipends
- Paid Family Leave
- Social impact volunteer time
- Catered lunches in the office
Insurify is committed to offering a fair, competitive, and transparent compensation program that supports our mission to attract, retain, and motivate top talent. Our compensation philosophy is guided by several factors including a candidate’s relevant experience, education/training, job\-related skills, and location.
In addition to the base salary our total compensation package includes health coverage, retirement contributions, and additional wellbeing benefits. Some positions may be eligible for company equity.
Below is the base compensation range for US locations:
$120,000\-$150,000
*We are proud to be* *an Equal Employment Opportunity and Affirmative Action employer.*
Salary Context
This $120K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Insurify, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($135K) sits 25% below the category median. Disclosed range: $120K to $150K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Insurify AI Hiring
Insurify has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.