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About This Role
About the Company
Rain is the global stablecoin payments platform for enterprises, neobanks, platforms, developers, and AI agents. Our technology allows partners to move, store, and use stablecoins instantly and compliantly through global payment cards, rewards, on/offramps, wallets, and cross-border rails. As both a Visa and Mastercard Principal Member, Rain issues cards that work at more than 175 million merchant locations in over 220 countries and territories. Built natively for stablecoins and trusted by more than 100 organizations worldwide, Rain delivers secure, scalable infrastructure that makes money move freely and instantly around the world.
You will have the opportunity to deliver massive impact at a hypergrowth company backed by some of the top investors in fintech, crypto, and SaaS. In January 2026, we closed a $250M Series C led by ICONIQ, valuing Rain at $1.95B, with Sapphire Ventures, Dragonfly, Bessemer Venture Partners, Galaxy Ventures, FirstMark, Lightspeed, Norwest, and Endeavor Catalyst also participating. If you're curious, bold, and excited to help shape a borderless financial future, we'd love to talk.
Our Ethos
We believe in an open and flat structure. You will be able to grow into the role that most aligns with your goals. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company.
About the Role
AI agents are already booking travel, subscribing to software, running procurement end to end, and moving money on behalf of users — much of it through Rain. Our Agent Control Layer is the governing surface for that activity: it enforces approved counterparties, amounts, merchants, and task scope at initiation, before a transaction ever occurs, rather than reconciling damage after the fact.
We are hiring a Senior Product Manager to own that platform. This is a 0-to-1 role inside a category being defined in real time. You will own the developer-facing surface — APIs, SDKs, controls, docs — that teams building agents use to give them spending power safely. McKinsey projects $3–$5 trillion in global agentic commerce by 2030. The primitives that make it trustworthy have not been built yet. You will build them.
You will report to the Head of Product and work daily with engineering, risk and compliance, network partnerships, and the developers shipping on us.
What you'll do
- Define the primitives for agent authority: spend scopes, merchant and counterparty allowlists, velocity and amount ceilings, task-bound credentials, human-in-the-loop approvals, and clean revocation.
- Own scoped virtual cards for agents — cards provisioned for a single merchant, amount, and task, retired the moment the task completes.
- Extend the rest of Rain's stack (wallets, virtual accounts, onramps, offramps, cross-border payments) to agent initiators within the parameters our customers define.
- Treat the API as the product. Own endpoint design, error semantics, idempotency, webhooks, sandbox and simulation tooling, SDKs, and reference agents.
- Own documentation and the onboarding path with the same rigor as the product itself. Time-to-first-authorized-agent-transaction is your metric.
- Run a tight design-partner loop with agent builders — read their integration code, sit in their debugging sessions, ship against what you find.
- Build the roadmap from first principles: form a thesis on where agentic commerce goes next, sequence bets accordingly, and kill what isn't working quickly.
- Partner with risk and compliance on Know Your Agent — agent identity, attestation, delegated authority, auditability, and dispute handling in a world where the cardholder wasn't the one who clicked.
- Represent Rain's technical point of view in Agentic Payments Alliance working groups and with Visa, Mastercard, and stablecoin partners as standards take shape.
- Write the specs, launch posts, and positioning that teach the market what controlled agentic payments actually means.
You’ll likely succeed if you have
- 8+ years in product management, with meaningful time on a developer-facing platform, API, or infrastructure product. You have shipped something engineers chose to build on.
- Technical depth. You read API diffs, write your own queries, prototype against your own endpoints, and hold your own in an architecture review. You don't need to have been an engineer; you do need engineers to take you seriously.
- 0-to-1 instincts. You have taken a product from a blank page to real usage in an ambiguous market, and you're comfortable with a roadmap that changes because the ground moved.
- Payments or fintech fluency, or the obvious appetite to acquire it fast. Authorization flows, issuing, interchange, chargebacks, settlement, KYC/KYB - you know these, or you'll learn them in weeks rather than quarters.
- Judgment under risk. Every control you design trades capability against loss. You can reason about that tradeoff explicitly and defend where you landed.
- Strong writing. At Rain, specs, docs, and launch narratives are the job, not overhead.
- Bias for motion. You would rather ship a narrow thing this month and learn from it than scope a broad thing for two quarters.
Bonus attributes
- Hands-on experience building or deploying AI agents - tool use, orchestration frameworks, MCP, evals.
- Stablecoin or onchain payments background: settlement, custody, treasury operations, or wallet infrastructure.
- Experience working directly with card networks, issuing processors, or sponsor banks.
- Time at an early-stage company during a period of fast scaling.
Things that enable a fulfilling, healthy and happy experience at Rain:
Unlimited time off Unlimited vacation can be daunting, so we require Rainmakers to take 10 days minimum for themselves.
Flexible working We support a flexible workplace – work from home, come into an office, or both. We want everyone to work in an environment where they're their most confident and productive selves. New Rainmakers receive a stipend to set up a comfortable home workspace.
Easy to access benefits For US Rainmakers, we cover 95% of your health, dental, and vision plan costs and 90% for your dependents, plus a 100% company-subsidized life insurance plan.
Retirement goals Plan for the future with confidence. We offer a 401(k) with a 4% company match.
Equity plan Every Rainmaker gets an equity option plan so we all benefit from our success.
Health and Wellness High performance begins from within. Rainmakers receive a monthly stipend to be used for eligible health and wellness spending like gym memberships/fitness classes, massages, acupuncture - whatever recharges you!
In-office meals Rainmakers working from the office enjoy lunch and dinner on us, covered with a DoorDash credit.
Team summits Summits play an important role at Rain. Time together helps us build relationships and a common destiny. Expect team and company offsites, both domestic and international.
Compensation Range: $200K - $250K
Salary Context
This $200K-$250K range is above the 75th percentile 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 Rain, 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 ($225K) sits 6% above the category median. Disclosed range: $200K to $250K.
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.
Rain AI Hiring
Rain has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, New York, NY. Compensation range: $250K - $250K.
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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