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About This Role
TheGuarantors is a cutting edge fintech company setting the standard in rent coverage with unrivaled insurance products. With a deep understanding of owner, operator, and renter needs, we believe renters deserve better access to the home of their dreams and operators deserve greater protection and growth opportunities. That’s why we’re leveraging our expertise in real estate and using AI\-based technology to help operators qualify renters faster while mitigating the risk of rental income loss. With $5B\+ in rent and deposits guaranteed, we work with 9 of the country’s top 10 operators and have been named one of Inc. 5000’s fastest\-growing companies, one of Forbes’ Best Startup Employers, and one of Deloitte’s Technology Fast 500\.
The AI Solutions \& Implementation Manager is responsible for helping define, prioritize, implement, and scale AI\-enabled solutions across TheGuarantors’ business. This role supports the AI function by identifying high\-impact automation opportunities, translating business processes into clear requirements, managing pilots, coordinating implementation, and measuring business impact.
The ideal candidate will bring strong operational judgment, product thinking, analytical rigor, and cross\-functional execution experience. This person will work closely with Data Engineering, Software Engineering, Data Science, Business Analytics, Claims, Underwriting, Ops, Finance, vendors, and stakeholders to move AI initiatives from idea to pilot to production.
Location
This is a hybrid role based in NYC
What You’ll Do
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- Own and manage implementation plans for AI\-enabled workflow improvements across key operational areas.
- Structure ambiguous business problems into clear opportunity areas, work plans, milestones, risks, and decision points.
- Build business cases for AI initiatives, including opportunity sizing, operational impact, implementation effort, cost, ROI, and realized benefit tracking.
- Translate business processes into workflow maps, requirements, success metrics, operating procedures, and implementation plans.
- Analyze operational data, process performance, and stakeholder input to identify automation opportunities and prioritize the highest\-impact work.
- Lead cross\-functional execution across Data Engineering, Software Engineering, Data Science, Business Analytics, Claims, Underwriting, Ops, Finance, vendors, and stakeholders
- Manage vendor pilots and implementation workstreams, ensuring clear scope, success criteria, timelines, ownership, and measurable outcomes.
- Create executive\-ready materials, including roadmap updates, decision memos, business cases, KPI reporting, and implementation readouts.
- Communicate updates, risks, blockers, recommendations, and business impact clearly and proactively.
- Use data, judgment, and business context to inform prioritization, solution design, implementation decisions, and recommendations.
- Partner with frontline teams to drive adoption, collect feedback, identify failure modes, and improve workflows after launch.
- Ensure AI initiatives are completed in accordance with applicable policies, procedures, standards, and regulatory requirements.
- Perform other role\-related responsibilities as assigned and aligned with business needs.
What You Bring
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- 3\+ years of experience in management consulting, business operations, corporate strategy, product operations, implementation, analytics, or a similar high\-impact execution role.
- Experience in structuring ambiguous problems, conducting analysis, developing recommendations, and driving cross\-functional execution.
- Experience building business cases, sizing opportunities, defining KPIs, managing work plans, and communicating recommendations to senior stakeholders.
- Strong understanding of business processes, operational performance, data\-driven decision\-making, and business impact measurement.
- Demonstrated ability to manage cross\-functional initiatives, partner with stakeholders, and deliver high\-quality work in a dynamic business environment.
- Bachelor’s degree in Business, Economics, Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
Preferred Qualifications
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- Experience working in management consulting, private equity portfolio operations, business operations, product strategy, or a high\-growth operating environment.
- Experience using systems and tools such as Zendesk, HubSpot, Decagon, Sierra, Ada, Claude, OpenAI, Snowflake, dbt, Power BI, GitHub, or similar platforms.
- Additional education, certification, or training in analytics, AI, automation, operations, product management, implementation, or strategy.
- Prior experience in fintech, insurance, proptech, real estate operations, claims, underwriting, customer support automation, or workflow transformation.
Benefits
- Opportunities to make an impact within a fast growing company
- Medical, dental, \& vision insurance, beginning day one
- Health savings account with employer contribution
- Flexible spending accounts (healthcare, dependent care, commuter)
- 401(k)
- Generous PTO and paid holidays
- In\-office lunch Perk
- Flexible working hours
- Paid parental leave
- Company sponsored short and long term disability
Base Salary
The base salary range is between $180,000 \- $200,000 annually.
Base salary does not include other forms of compensation or benefits. Final offer amounts are determined by multiple factors, including prior experience, expertise, location and current market data and may vary from the range above.
TheGuarantors is an Equal Opportunity Employer. We celebrate diversity and are committed to an inclusive environment for all.
Salary Context
This $180K-$200K range is above the median 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 TheGuarantors, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($190K) sits 5% above the category median. Disclosed range: $180K to $200K.
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.
TheGuarantors AI Hiring
TheGuarantors has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $230K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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
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