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About This Role
About Pagaya
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 Avon AI
Avon AI was recently acquired by Pagaya, which backs our growth. Avon AI is the management and control platform for enterprise conversational AI. Enterprises are deploying AI agents faster than they can govern them. Avon AI gives them one place to manage those agents and the knowledge behind them: continuous quality monitoring of real conversations, proactive testing and simulation before changes ship, knowledge-base management, and real-time guardrails that sit inline in the conversation path to block, redact, or correct what an agent says.
We are agent-agnostic. Avon AI works with agents our customers built themselves and with third-party agents they only integrate with, across any framework or vendor. Customers run us as SaaS on AWS or deploy the full platform inside their own private cloud when regulation demands it. We are a small, fast-moving team based in Tel Aviv, already deployed at banks and insurers, and working with enterprise customers in finance, healthcare, insurance, travel, and more.
About the role
You will be one of the first hires in our sales and marketing organization and our first person on the ground in the US. This is a player-coach role: you carry a quota and close enterprise deals yourself, and as the pipeline grows you help build the US team around you.
You own the go-to-market motion end to end: generating demand, finding the champion inside a large regulated organization, mapping the stakeholders, running a complex sales process, and getting to a signed contract. You will work directly with the founders and have real influence over how we sell, what we say, and who we hire next.
What you will do
Sales
- Own the full sales cycle with enterprise accounts in the US, from first conversation to closed deal, with a focus on banks, insurers, and other regulated companies.
- Identify champions, map buying committees (business, risk, IT, procurement), and run multi-threaded processes that reach sign-off.
- Run discovery and demos, scope pilots on the customer's real production conversations, and convert pilots into production contracts.
- Manage the pipeline in the CRM with accurate forecasting.
Marketing and demand generation
- Build the top of the funnel: outbound campaigns, events, webinars, partner referrals, and content.
- Work with partners and resellers, including consulting firms and the AWS Marketplace, to source and accelerate deals.
- Sharpen our positioning and messaging for the US market based on what you hear from buyers.
Build the organization
- Define the sales process, playbook, and qualification criteria we will scale with.
- Help recruit and onboard the next sales and marketing hires as the US business grows.
- Feed structured customer and market input back to the product team.
What we are looking for
- A US-based seller with a track record of closing enterprise software deals with large organizations, ideally in regulated industries such as banking, insurance, or healthcare.
- Experience selling into financial companies is a strong advantage.
- Startup background: you have sold an early-stage product without a large brand behind you and are comfortable building from little.
- "Full stack" go-to-market: you can prospect, run marketing activities, present to executives, and negotiate contracts yourself.
- Comfortable with technical products. You can explain AI agents, quality monitoring, and deployment options (SaaS and private cloud) credibly to both business and IT buyers.
- Self-directed and structured: you set your own priorities, build process where none exists, and report clearly.
- A connection to Israel, or experience working with an Israeli headquarters, is a plus.
Why join
- A real product with paying enterprise customers in production, not a pitch deck.
- The chance to own a market: you define how Avon AI sells in the US and build the team that follows.
- Direct work with the founders and a short line between customer feedback and product decisions.
Location & Engagement: Our primary preference is a full-time role based out of our NYC office (Monday-Thursday), collaborating with the team in Tel Aviv.
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
$135,000 - $155,000 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 $135K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $135K to $155K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
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: $155K - $155K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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