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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
As an Applied AI Engineer embedded within the Office of the CEO and President (OCEO/OOP), you will serve as a high\-impact builder driving the future of AI\-enabled business transformation at Pagaya. Working at the intersection of business strategy, data architecture, and modern artificial intelligence, you will build and deploy practical, high\-value AI solutions, automations, and intelligent agents that modernize internal workflows.
Starting within the OCEO/OOP to address high\-visibility executive priorities, your work will quickly expand to partner with key business functions across Capital Markets, Finance, and broader Operations. Combining a strong background in Business Intelligence (BI) with hands\-on AI engineering capabilities, you will translate complex financial workflows into durable, scalable AI systems that elevate operational speed and analytical precision across the company.
### Responsibilities
- Embedded AI Development: Act as the primary technical builder, advisor, and problem\-solver for AI\-enabled workflow transformations \- starting in the OCEO/OOP and expanding into Capital Markets, Finance, and key operational units.
- Workflow Automation \& Agentic Systems: Identify manual, repeated, or data\-dense business processes and build production\-ready AI tools, customized copilots, and agentic workflows to streamline execution.
- BI \& Data Integration: Leverage a robust background in Business Intelligence and data analysis to connect LLMs, foundation models, and AI agent frameworks directly into Pagaya’s data infrastructure, dashboards, and financial models.
- Cross\-Functional Execution: Work closely with business leadership, financial analysts, and capital markets teams to translate ambiguous business problems and complex financial data into practical, user\-friendly technical solutions.
- Rapid Prototyping \& Scale: Design, iterate, and deploy prototypes rapidly based on stakeholder feedback, while adhering to enterprise standards for data security, governance, and responsible AI usage.
- Enablement \& Documentation: Templatize and document solutions to enable scalable reuse across departments; lead walkthroughs and light enablement to drive active user adoption across technical and non\-technical teams.
### Requirements
- Experience: 4\+ years of experience in data engineering, business intelligence, or software engineering with demonstrated hands\-on experience building applied AI solutions, internal tools, or AI\-driven automations.
- Business Intelligence Core: Strong mastery of SQL, data modeling, BI tools (e.g., Tableau, PowerBI, Looker), and experience processing, analyzing, and structuring large data sets.
- Technical \& Coding Proficiency: Advanced proficiency in Python (or JavaScript/TypeScript) for data manipulation, backend scripting, and rapid software prototyping; comfort generating, debugging, and refactoring AI\-generated code.
- Modern AI Tooling \& Frameworks: Hands\-on experience with modern LLM APIs/ecosystems (OpenAI, Claude, Gemini), agentic orchestration frameworks, RAG (Retrieval\-Augmented Generation) architectures, and enterprise AI platforms.
- Communication \& Stakeholder Management: Exceptional written and verbal communication skills with a proven track record of distilling technical and financial concepts for non\-technical leadership and cross\-functional teams.
- Adaptability \& Drive: An entrepreneurial mindset with the ability to manage multiple complex, fast\-paced projects simultaneously, delivering meticulously accurate, self\-audited work within tight deadlines.
- Education: Bachelor’s degree in Computer Science, Data Science, Information Systems, Finance, Engineering, or an equivalent quantitative discipline required.
- Preferred: Prior exposure to financial services, structured finance, fixed income, or capital markets workflows is a strong plus.
*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 \- $170,000 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-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($155K) sits 28% below the category median. Disclosed range: $140K to $170K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
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: $170K - $170K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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