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About This Role
About Pagaya
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Shape the Future of Finance
Pagaya is building a leading artificial intelligence network to help our partners grow their businesses and better serve their customers.
Pagaya is a global technology company making life\-changing financial products and services available to more people nationwide, as it reshapes the financial services ecosystem. By using machine learning, a vast data network and a sophisticated AI\-driven approach, Pagaya provides comprehensive consumer credit and residential real estate solutions for its partners, their customers, and investors. Its proprietary API and capital solutions integrate into its network of partners to deliver seamless user experiences and greater access to the mainstream economy. Pagaya has offices in New York and Tel Aviv. For more information, visit pagaya.com.
Let's create better outcomes together!
### About the Role
We are seeking a AI Transformation \& Operations Lead to drive process re\-design, automation, and scaling of Artificial Intelligence across our Corporate Finance organization. This position sits at the intersection of Finance, Data, and Operations, acting as a hands\-on partner to our global finance teams (including FP\&A, Treasury, Accounting, and Finance Operations) to analyze current workflows, identify high\-impact automation opportunities, and aggressively embed AI tools into our daily operations.
The core mission is to modernize and streamline our financial workflows for maximum speed, accuracy, and scalability. A primary focus will be establishing a reusable Finance Knowledge Infrastructure—capturing processes, decision logic, and data flows in a structured way that eliminates tribal knowledge and enables robust AI augmentation. The ideal candidate brings a blend of corporate finance acumen, strong project management skills, and a passion for AI\-driven business transformation.
### Responsibilities:
#### 1\. Finance AI \& Automation Transformation
- Partner closely with FP\&A, Treasury, Accounting, and Finance Operations teams to map end\-to\-end workflows, diagnose operational bottlenecks, and identify pain points.
- Translate complex financial challenges into scalable AI and data solutions, moving initiatives smoothly from concept to production.
- Ensure solutions are deeply embedded into day\-to\-day workflows, prioritizing comprehensive, end\-to\-end operational improvements over standalone tools.
#### 2\. Finance Knowledge Infrastructure \& Process Engineering
- Build and maintain a structured finance knowledge library capturing process documentation, business rules, reporting definitions, and reusable analytical templates.
- Enable AI systems and human teams to access consistent, high\-quality institutional knowledge, reducing reliance on fragmented documentation.
- Continuously recommend and roll out structural improvements to maximize efficiency, data quality, and compliance controls.
#### 3\. Opportunity Discovery, Prioritization \& ROI Tracking
- Conduct structured "AI opportunity audits" across finance functions, maintaining a clear, evolving transformation roadmap.
- Evaluate and prioritize initiatives through an investment\-led framework, quantifying impact across time savings, cost reduction, risk mitigation, and decision speed.
- Own the delivery of 5–10 high\-value AI and automation initiatives annually, tracking and reporting realized value to executive leadership.
#### 4\. Cross\-Functional Collaboration \& Tech Alignment
- Be a part of a global team of AI specialists within the organization and partner closely with Engineering, Data Science, and IT teams to design, test, and deploy AI\-driven workflows and technical toolings customized for finance.
- Evaluate and optimize the usage of the core technology stack, ensuring clear integration between standard enterprise software and next\-gen productivity tools.
- Serve as a bridge between global teams (e.g., U.S. and international offices) to ensure seamless operational alignment across time zones and leadership priorities.
#### 5\. Enablement, Change Leadership \& Evangelism
- Act as a champion for AI adoption across the finance department and guiding the finance team on where they can automate workflows and implement tooling
- Equip finance teams with the tools, playbooks, and training required to independently utilize AI\-enabled tools, fostering a culture of continuous learning and experimentation.
- Provide high\-quality presentations, executive dashboards, and clear cross\-functional updates highlighting project progress, risks, and strategic opportunities.
### Qualifications:
- Experience: 5\+ years of experience in Corporate Finance, FP\&A, Treasury, Accounting, Finance Operations, Business Operations, or Management Consulting.
- AI \& Automation Track Record: Proven experience implementing AI tools, workflows, or software automation within a business context to drive tangible efficiency gains.
- Technical Fluency: Distinct experience designing or overseeing processes using:
- + *AI \& Productivity Tools:* (e.g., Claude, Gemini, ChatGPT, Cursor, or similar developer/LLM interfaces).
+ *Enterprise Infrastructure \& Automation:* CRM, ERP, and automation platforms (e.g., Salesforce, Workato, Jira, Gong, or financial systems equivalents).
+ *Database and Data Management Experience, specifically with Snowflake*
- Cross\-Functional Leadership: Strong experience partnering with technical teams (Engineering, IT, Data Science) as well as non\-technical stakeholders. Ability to influence without authority and drive accountability across multiple functions and seniority levels.
- Finance Domain Expertise: Strong understanding of how corporate finance processes, reporting structures, and decision\-making operate end\-to\-end.
- Communication Skills: Excellent written and verbal communicator able to translate complex data, technical operations, and financial analyses into clear, actionable reporting for senior leadership.
- Mindset: A global mindset with experience collaborating across international teams. High bias toward execution, structured program management, and data\-driven problem\-solving.
- Education: Bachelor’s degree in Finance, Economics, Business Operations, Information Systems, or a related field.
*The pay ranges for New York\-based hires are commensurate with candidate experience.*
*Pay ranges for candidates working in locations other than New York may differ based on the cost of labor in that location.*
Compensation Range for New York Based Hires
$140,000 \- $180,000 USD
Our Team
Pagaya was founded in 2016 by seasoned research, finance, and technology entrepreneurs with our head quarters located in NYC and Tel Aviv.
We move fast and smart, identifying new opportunities and building end\-to\-end solutions from AI models and unique data sources. Every Pagaya team member is solving new and exciting challenges every day in a culture based on partnership, collaboration, and community.
Join a team of builders who are working every day to enable better outcomes for our partners and their customers.
Our Values
- Continuously Learn\- We challenge ourselves for the sake of getting better as individuals, as teams, and as an organization to deliver for our partners.
- Debate and Commit\- We respectfully and openly debate to strengthen our ideas and build shared conviction \- once we decide, we go all in, together.
- Dream Big and Act\- We boldly tackle complex problems, pressure\-test solutions in real\-time, and adapt with speed and energy.
- Advance Inclusion\- We create a world where everyone can win, designing systems that better represent people and generate sustainable value for our employees, partners and investors.
- Be Accountable Together\- We proudly own our actions and our results, taking initiative to ensure our work gets over the finish line as a team.
More than just a job
We believe health, happiness, and productivity go hand\-in\-hand. That's why we're continually looking to enhance the ways we support you with benefits programs and perks that allow every Pagayan to do the best work of their life.
Salary Context
This $140K-$180K range is below 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 Pagaya Investments, 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 ($160K) sits 12% below the category median. Disclosed range: $140K to $180K.
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.
Pagaya Investments AI Hiring
Pagaya Investments has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
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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