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Director of Product – AI Underwriting
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- Full\-Time
- Location: Remote (US) \- core Pacific Time working hours required; approximately 20% travel to our San Ramon office and to customers.
- Compensation: The base salary range for this full\-time position is $160,000 – $200,000 \+ 15% target performance\-based bonus. The position is eligible for performance\-based equity compensation.
### About ARIVE
ARIVE (arive.com) is the industry's first integrated LOS, POS, and Pricing Engine with a connected Lender Marketplace, purpose\-built for independent mortgage originators. We are a dynamic high\-growth company revolutionizing digital mortgage originations, and our platform seamlessly connects independent loan originators, top\-tier wholesale lenders, borrowers, referral partners, credit providers, and property and settlement service providers \- creating a faster, more transparent, and more affordable path to homeownership.
### About ARIVE AI
ARIVE AI brings agents into the core of the platform \- built in, not bolted on. Its underwriting agent runs guideline\-grounded rules across live loan files to produce Pre\-Underwrite outputs: findings, checklists, and conditions that surface eligibility issues and documentation gaps. The agents also try to autonomously address any issues before a file ever reaches the lender underwriting. The result is cleaner files into every lender's process \- and it only works if the rules behind it are right.
### Position Overview
The Director of Product \- AI Underwriting owns the correctness of ARIVE AI's Underwriting Rules and Pre\-Underwrite outputs. This is an underwriting\-expert\-first role for someone who also understands the impact of AI: you bring deep, hands\-on underwriting analysis and working AI fluency — reviewing prompts, shaping evaluation criteria, and reasoning about agent behavior — while our engineering team owns the low\-level LLM development and evaluation infrastructure. You will verify that every rule ARIVE AI generates and applies is faithful to the agency guides and to each lender's specific guidelines and overlays, working directly with lenders and agencies to review rules — and you will stand behind what the Pre\-Underwrite says.This is a remote position with core Pacific Time working hours, working closely with the ARIVE platform engineering team and reporting to executive leadership and to the Head of Product once that role is hired. You will start as a hands\-on individual contributor, with the potential to grow a team as the domain scales.
Key Responsibilities
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- Rules Review \& Verification: Review, test, and verify ARIVE AI Underwriting Rules against the agency guides and against each investor's guidelines and overlays. Approve rules before release and re\-verify them as guidelines change. Verify the agents' autonomous actions with the same rigor \- every automated issue resolution must be correct and guideline\-compliant.
- Lender \& Agency Validation: Work directly with lender credit and underwriting teams, and with the agencies as applicable, to confirm rule accuracy and correctness — run validation reviews, leverage findings, reconcile interpretation differences, and maintain lender\-specific rule sets as the source of truth.
- Pre\-Underwrite Output Ownership: Be responsible for Pre\-Underwrite outputs end to end: audit findings, checklists, conditions, and the agents' autonomous issue resolutions for accuracy on real files; triage errors and drive root\-cause fixes; own the accuracy bar for what "correct" means.
- Underwriting Depth: Direct the rules' treatment of income, asset, credit, and collateral underwriting across agency and Non\-QM programs \- the full expertise map below.
- AI Development Partnership: Work daily with platform engineers — who own low\-level LLM development and evaluation infrastructure \- to review and refine agent prompts, build evaluation datasets from the guides, design human\-in\-the\-loop review and escalation, and validate agent output on messy, real\-world files.
- Guideline Change Management: Monitor agency and lender guideline updates and drive timely, verified rule changes so Pre\-Underwrite outputs never drift from current policy.
- Roadmap \& Launch Partnership: Shape the AI Underwriting roadmap and partner closely with Product, Engineering, Sales, Onboarding, Support, Compliance, and Executive Leadership to successfully launch new capabilities, integrations, and strategic initiativesMetrics \& Quality: Define and report the domain's quality KPIs — rule accuracy, false findings, condition quality, and lender acceptance of Pre\-Underwrite outputs.
### Underwriting Expertise
This role demands full\-file depth across all four underwriting disciplines:
- Income Underwriting: Wage\-earner and self\-employed income analysis (Forms 1084/1088, tax return and K\-1 analysis), rental income, and Non\-QM income methods \- bank statement, P\&L, 1099, DSCR, and asset\-utilization/depletion programs.
- Asset Underwriting: Asset sourcing and seasoning, large deposits, gift funds, business funds, earnest money, and reserve requirements across programs.
- Credit Underwriting: Tradeline and derogatory\-event analysis (waiting periods, extenuating circumstances), liabilities and DTI treatment, inquiries and letters of explanation, and DU/LPA findings interpretation and conditioning.
- Collateral Underwriting: Appraisal review, property eligibility (including condos and unique property types), comparable and adjustment analysis, subject\-property condition, and appraisal\-risk flags (e.g., CU).
Breadth matters as much as depth: ARIVE AI's rules must hold up across agency and Non\-QM programs — and Non\-QM is exactly where lender\-specific guidelines diverge most.
### Qualifications
- Must have: deep, demonstrated experience in detailed underwriting analysis — full\-file review spanning the income, asset, credit, and collateral disciplines above — at senior scope.
- 10\+ years in mortgage underwriting and/or agency credit policy. A Bachelors or Masters degree is a strong plus.
- Mastery of agency guidelines — Fannie Mae Selling Guide, Freddie Mac Seller/Servicer Guide, and FHA Handbook 4000\.1 — with VA and USDA experience valued; Non\-QM underwriting experience strongly preferred.
- Wholesale/TPO or correspondent lending background strongly preferred, including collaboration with lender underwriters, account executives, setup teams, etc.
- Working knowledge of AI/LLM platforms and tools, and a genuine passion for technology. Should be able to review prompts, reason about agent behavior, and contribute to evaluations — plus familiarity with modern underwriting technology (AUS, income/asset verification tools, AI rules/analysis engines).
- Willingness to travel approximately 20% — to our San Ramon office and to lender and agency partners. Bay Area–local candidates able to work regularly from the office are a strong plus.
### What We Offer
- Compensation: The base salary range for this full\-time position is $160,000 – $200,000 \+ 15% target performance\-based bonus. The position is eligible for performance\-based equity compensation. Our salary ranges are determined by location, level, and role; individual compensation will be determined by experience, skills, and job\-related knowledge.
- Comprehensive health, dental, and vision coverage.
- 401(k) with employer match.
- Flexible PTO.
- Work closely with the ARIVE platform engineering team, owning a flagship domain of the platform.
*Note: ARIVE is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status or any other characteristic protected by local, state, or federal laws, rules, or regulations.*
Salary Context
This $160K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Arive, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $160K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Arive AI Hiring
Arive has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Ramon, CA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
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