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About the role:
As Mercury continues its rapid growth trajectory, navigating an increasingly complex regulatory, partner, and product landscape is critical. Compliance is not only a requirement, but a competitive advantage that enables Mercury to move faster, build trust, and launch innovative products responsibly.
Nowhere is that more true than in AI. We believe we have a path to lead the US banking* industry into the AI era, and that how we choose to build and deploy AI will define our success over the next three years.
We are building first-party agentic products, headless and agent-accessible banking through our API and MCP, and the internal platforms that let us build with AI at speed. Core to all of it is great governance—these systems must run on one trusted system of access, logging, and data controls that meets the requirements of our banking ambitions.
We are seeking a Lead Compliance Risk Manager to serve as the compliance advisor for Mercury's AI products. This is a new, high-visibility role and the first dedicated compliance advisory seat covering AI at Mercury. You will be the leading authority on AI product compliance risk for the Compliance Team—shaping how we build, how we govern, and how we explain ourselves to our partners and regulators.
You will partner closely with Product, Engineering, Risk, Data, Legal, and Operations to ensure Mercury's AI products are compliant-by-design from concept through launch and scale. You will also help define the AI governance framework itself: the standards, controls, escalation paths, and documentation that allow a bank-partnered fintech to deploy agentic capabilities responsibly and at pace.
How this role fits alongside our other AI roles: this is a product compliance advisory role. Our Data & AI Governance team owns enterprise governance frameworks, responsible-AI standards, AI use case intake and inventory, and risk classification; Model Risk Management owns model inventory, tiering, and validation. You will be a close partner to and a major consumer of both—running our AI products through their processes, applying their standards to real product decisions, and bringing them the product-level detail they need to see. You will also help extend our existing product compliance playbooks to cover AI features, in partnership with your manager and the rest of the Product Compliance team. Your mandate is the products, not the program.
In this role, you will:
- Serve as the subject matter expert and primary compliance advisor for Mercury's AI and agentic products, including Command, our API, and agentic access methods such as MCP and CLI.
- Work closely with cross-functional risk and product teams to bring Mercury's AI products to market in a way that meets all relevant financial partner, regulatory compliance obligations, and our AI governance standards.
- Ensure all processes and products under your area of responsibility comply with applicable rules and regulations. Stay up to date with changes to applicable regulations, supervisory guidance, and emerging AI-specific law, and implement necessary updates to impacted products to maintain compliance.
- Support expansion for new product launches by scoping, defining, and communicating product and compliance requirements.
- Work cross-functionally to drive remediation efforts for products within your defined scope.
- Support the compliance function in defining and achieving departmental initiatives, maintaining relevant policies and procedures, developing documentation solutions, and liaising with auditors.
- Mentor and provide strategic guidance to compliance colleagues as AI capabilities spread across every product domain, and contribute to building a strong culture of compliance across Mercury.
- Work closely with our Risk team, which owns AI governance, as Compliance’s AI product expert to contribute product-level guidance to ongoing AI governance updates.
Core responsibilities:
- Partner closely with Product, Engineering, Risk, Data, Operations, and Legal to bring Mercury's AI products to market in a manner that meets all relevant regulatory, financial partner, and network obligations.
- Advise product and engineering teams throughout the product lifecycle—discovery, design, evaluation, launch, and iteration—to ensure AI products are compliant-by-design rather than reviewed after the fact.
- Identify, assess, and document compliance risks specific to AI and agentic systems—including inaccurate or fabricated output, unauthorized or over-scoped agent action, inadequate consent and disclosure, unexplainable decisioning, disparate impact, data leakage across customers or organizations, prompt injection and third-party agent risk, and insufficient audit trail—and work cross-functionally to design pragmatic mitigation strategies.
- Define the compliance requirements for the platforms AI is built on, in partnership with the teams that own them: the LLM gateway all internal traffic routes through, the internal app platform, agent orchestration frameworks, and the data access and governance layer.
- Establish and refine AI-specific compliance standards for the department—what evidence a launch requires, what must be logged, what requires human review, what must be escalated, and what is out of bounds—and ensure those standards evolve as the organization and the technology grow.
- Support new product launches and expansions by scoping, defining, and communicating compliance requirements clearly and early, including for co-marketing and distribution partnerships with third-party AI platforms.
- Monitor regulatory, supervisory, and industry developments relevant to AI in financial services, assess impact, and drive implementation of required changes.
- Own and oversee multiple concurrent high-impact AI compliance workstreams, holding project owners accountable for results and ensuring each aligns with department and company objectives.
- Support remediation efforts for products within scope, including root cause analysis, corrective action planning, and execution tracking.
- Partner with executive leadership, partner bank compliance teams, and external advisors to guide high-level decisions on AI direction, presenting data-driven recommendations.
- Contribute to the broader Compliance function by supporting policy development, documentation standards, audit preparation, and examiner interactions.
- Help build and reinforce a strong culture of compliance across Mercury through collaboration, education, and influence—including raising the AI fluency of the Compliance team itself, and developing scalable tools, templates, and frameworks others can reuse.
You should have:
- 8+ years of experience in a compliance advisory, product compliance, or regulatory risk role, ideally within fintech or financial services, with a track record of being the recognized authority in your domain.
- Demonstrated experience advising on AI, machine learning, or model-driven products in a regulated environment—or comparable experience standing up a new compliance domain where no playbook existed.
- Working knowledge of the regulatory and supervisory framework relevant to AI in banking, including UDAAP, fair lending and ECOA/Reg B, FCRA and adverse action, Reg E and payments authorization, BSA/AML, GLBA and privacy, marketing and disclosure requirements, recordkeeping, complaint management, model risk management expectations, and third-party and vendor risk management guidance.
- Familiarity with emerging AI-specific regimes and frameworks and the ability to form a practical view on their application—such as the NIST AI Risk Management Framework, state AI and automated decisioning laws.
- Technical fluency to engage credibly with engineers on model behavior, evaluation and testing, agent permissioning and tool scoping, logging and observability, and data access controls—and to know which questions actually surface risk.
- Deep experience partnering with product and engineering teams on regulatory and compliance matters across the product lifecycle, including on fast-moving launches with external dependencies.
- Proven ability to own highly ambiguous, large-scale compliance problems and to create and execute a strategy to address them, without waiting for precedent.
- Experience designing compliance frameworks that span multiple product lines or departments, and improving systems and processes so that compliance posture evolves alongside business growth.
- Demonstrated success operating with senior stakeholders, including executives, partner banks, auditors, or regulators, and presenting data-driven recommendations that drive decisions.
- Strong project management skills, including the ability to define problems, create execution plans, manage several high-impact initiatives at once, and drive outcomes.
- Excellent written and verbal communication skills, with the ability to translate regulatory and financial partner risk into terms stakeholder teams understand, and to resolve conflict around risk tolerance, resourcing, and timelines.
- Experience mentoring mid-level compliance professionals and raising the capability of a team, not just delivering your own work.
- Comfort operating independently in a fast-paced, ambiguous environment, with sound judgment on when to move and when to escalate.
- Curiosity, adaptability, and genuine interest in engaging with frontier technology and the regulatory challenges that come with it.
- Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.
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Total Rewards
The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.
Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.
Our target new hire base salary ranges for this role are the following:
US employees in New York City, Los Angeles, Seattle, or the San Francisco Bay Area:
$207,800 - $259,800 USD
US employees outside of New York City, Los Angeles, Seattle, or the San Francisco Bay Area:
$187,000 - $233,800 USD
Canadian employees (any location):
$196,400 - $245,500 CAD
Salary Context
This $196K-$259K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Mercury, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($228K) sits 8% above the category median. Disclosed range: $196K to $259K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Mercury AI Hiring
Mercury has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span New York, NY, New York, NY, US. Compensation range: $259K - $259K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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