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Mercury is seeking a Data \& AI Governance leader to build and spearhead an enterprise\-wide governance program for data and artificial intelligence. Reporting to the Chief Risk Officer, this leader will establish practical standards for how data and AI are owned, developed, used, protected, and monitored across the organization.
The ideal candidate combines strong governance and risk\-management experience with sufficient technical fluency to work effectively with Data, Engineering, Product, Information Security, Legal, Compliance, and business teams. This person should be comfortable building in a fast\-moving environment and designing governance that supports responsible innovation without creating unnecessary complexity.
This role will work closely with the Model Risk Management and Information Security teams while maintaining a distinct mandate: Data \& AI Governance will establish enterprise governance and responsible\-use standards, while Model Risk Management will retain responsibility for model inventory, tiering, validation, and model\-risk oversight.
### Key Responsibilities:
- Develop and implement Mercury’s enterprise Data and AI Governance frameworks, policies, standards, and operating model.
- Establish clear accountability for data ownership, stewardship, quality, lineage, classification, access, retention, and appropriate use.
- Create a risk\-based governance process for AI use cases across their lifecycle, including intake, assessment, approval, implementation, monitoring, and retirement.
- Develop responsible\-AI principles and standards addressing transparency, explainability, fairness, privacy, security, human oversight, reliability, and regulatory compliance.
- Maintain an enterprise inventory of material data assets, AI use cases, and related governance decisions in coordination with relevant stakeholders.
- Define risk\-based classifications and governance requirements based on the sensitivity, complexity, materiality, and customer or regulatory impact of each use case.
- Establish governance for internally developed, vendor\-provided, and embedded AI capabilities, including generative AI.
- Partner with various Product, Engineering, Data, and business teams to embed governance requirements into development and change\-management processes.
- Coordinate with Model Risk Management to determine when an AI use case meets the definition of a model and is subject to model\-risk requirements.
- Partner with Information Security and Technology Risk on data protection, cybersecurity, access, architecture, resilience, and technology\-control considerations.
- Partner with Legal and Compliance to identify and implement applicable regulatory, contractual, consumer\-protection, and privacy requirements.
- Develop processes for identifying, documenting, escalating, and remediating data\- and AI\-related risks and issues.
- Establish metrics/reporting to provide management and Board committees with visibility into data quality, governance maturity, AI adoption, exceptions, incidents, and emerging risks.
- Monitor regulatory developments, industry practices, and emerging risks related to data and AI, and translate them into proportionate governance expectations.
- Support relevant Data and AI governance forums/committees and facilitate timely, well\-documented decisions.
- Eventually, build and lead a high\-performing Data \& AI Governance team as the program matures.
- Promote a culture in which data is treated as an enterprise asset and AI is used responsibly, transparently, and in alignment with Mercury’s risk appetite.
### Qualifications:
- 10\+ years of relevant experience in data governance, AI governance, technology risk, information governance, model risk, privacy, compliance, or a related discipline.
- Demonstrated experience building or materially enhancing a data governance, AI governance, or responsible\-AI program.
- Strong understanding of data ownership, stewardship, quality, lineage, metadata, classification, access, retention, and lifecycle management.
- Working knowledge of AI and machine\-learning concepts, including generative AI, large language models, training and inference data, explainability, bias, performance monitoring, and human oversight.
- Experience developing practical, risk\-based policies and governance processes that can operate effectively in a fast\-moving technology environment.
- Ability to distinguish among data governance, AI governance, model risk, information security, privacy, and compliance responsibilities while coordinating effectively across those functions.
- Strong judgment and the ability to balance innovation, customer outcomes, regulatory expectations, and risk management.
- Demonstrated ability to influence senior executives, technical teams, and business leaders without relying solely on formal authority.
- Excellent written and verbal communication skills, including the ability to explain complex technical and risk concepts to executive and Board audiences.
- Experience leading teams and managing cross\-functional programs with multiple stakeholders.
- Strong 1LOD/2LOD judgment with an understanding of how enterprise Risk should govern, challenge, and partner with Engineering without taking ownership of 1LOD risks.
- Pragmatic judgment: Translates principles into workable processes and focuses on material risks over theoretical ones.
- Technical curiosity: Understands technical complexity while staying focused on business and customer outcomes.
- Decisive and collaborative: Makes sound decisions amid ambiguity, moves quickly, and challenges constructively across teams
### Preferred Qualifications:
- Experience within a fintech, financial institution, technology company, or other highly regulated environment.
- Familiarity with banking regulatory expectations for data management, model risk, third\-party risk, privacy, consumer protection, and information security.
- Experience with recognized data\- and AI\-governance frameworks and standards, such as DAMA\-DMBOK, NIST AI RMF, ISO/IEC 42001, or comparable frameworks.
- Experience governing third\-party data, vendor AI solutions, and embedded AI capabilities.
- Technical or analytical experience in data architecture, data engineering, machine learning, analytics, or software development.
- Experience operating in a company\-building or bank\-building environment
- Mercury is a fintech company, not an FDIC\-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
### Compensation:
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: $225,800 \- $282,300 USD
- US employees outside of New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $203,300 \- $254,100 USD
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.
\#LI\-DR1
Salary Context
This $203K-$282K range is above the 75th percentile 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($242K) sits 13% above the category median. Disclosed range: $203K to $282K.
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.
Mercury AI Hiring
Mercury has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $282K - $282K.
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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