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locations
NYC (1285\)
time type
Full time
posted on
Posted Yesterday
job requisition id
R7206### About The Team
### The Mizuho U.S. Operations (“MUSO”) Regulatory Affairs Group oversees MUSO’s regulatory relationships and serves as the central point of contact for both regulators and internal stakeholders. The Regulatory Affairs Group centrally manages MUSO’s regulatory engagement and supervisory priorities across US regulators, including examinations, information requests, continuous monitoring meetings, meeting preparation, training, supervisory commitments, the timely closure of remediation findings, horizon scanning for emerging regulations, regulatory change management and maintenance of the laws, rules and regulations library.
### Summary
### The Regulatory Affairs Group is seeking an AI Specialist for Regulatory Affairs who will lead the adoption, governance and risk management of artificial intelligence across MUSO’s regulatory change management and regulatory relations functions. The candidate will identify, develop and build out AI and Generative AI capabilities that enhance how MUSO scans for emerging regulations, manages regulatory change, maintains its laws, rules and regulations library, and engages with its regulators, while ensuring comprehensive AI governance and risk management requirements are met. The candidate will work collaboratively across the Compliance Department, lines of business and control functions, keeping senior leadership informed on the status of AI initiatives and key risk indicators.
### This role is hybrid and based in our New York City Office.
### Responsibilities
- Drive innovation and the development of AI\-based tools and functionality within the Regulatory Affairs Group to keep pace with the evolution of regulatory change management and regulatory relations in the age of AI
- Oversee and manage key AI initiatives within Regulatory Affairs, partnering with the Compliance Department, lines of business and control functions to identify, develop and build out enterprise\-wide capabilities while ensuring AI governance and risk management requirements are met
- Apply AI and Generative AI capabilities to horizon scanning, the identification and assessment of new and amended regulations, and the maintenance of the laws, rules and regulations library
- Leverage AI to streamline regulatory examinations, information requests, continuous monitoring meetings and other regulatory engagements, strengthening MUSO’s regulatory relationships
- Partner closely with subject matter experts in AI, Architecture \& Engineering and Data to complete impact assessments of new AI policies, standards and procedures, and to ensure they are effectively implemented and maintained
- Partner with Modeling and AI Governance teams to review and approve AI and Generative AI tools, products and platforms for use across regulatory change management and regulatory relations
- Support the design and implementation of AI governance management reporting and dashboards covering regulatory change and regulatory relations programs
- Act as liaison and quality control point between control managers, lines of business and regulators for Audit, Regulatory and client AI\-related requests
- Provide oversight and challenge to AI strategy, governance and product/platform implementation impacting regulatory affairs
- Monitor and facilitate oversight of compliance with AI\-related regulations and emerging supervisory expectations
- Conduct assessments of AI systems to identify and mitigate risks
- Collaborate with legal, technical, compliance and operational teams to address regulatory and AI compliance issues
- Provide training and support to staff on AI matters affecting regulatory change management and regulatory relations
- Promote a culture of continuous improvement in regulatory compliance and responsible AI practices across MUSO
### Qualifications
- Bachelor’s degree in Computer Science, Law, Business, or a related field
- 5 \- 10 years of experience in regulatory compliance, risk management, or governance within the financial services or a highly regulated industry sector
- Prior experience in AI/ML and Generative AI delivery in the financial services industry
- Strong understanding of AI technologies and their application within a compliance or regulatory environment
- Strong understanding of regulatory frameworks and industry standards
- Strong familiarity with the regulatory oversight process as it relates to FRB, CFTC, FINRA, NFA, SEC, and other U.S. regulators
- Strong knowledge of large financial institutions, businesses, products, and functions including banking and underwriting businesses, securities markets, legal, compliance, risk management, operations, and treasury
- Excellent analytical and problem\-solving skills
- Ability to collaborate across different functional and technical areas and to work collaboratively with cross\-functional teams
- Excellent verbal and written communication skills, including a high level of attention to detail
- Strong project management and critical thinking skills
- High proficiency with Microsoft Office, particularly Excel and PowerPoint; ability to work with SharePoint and GRC tools
- Ability to work independently and as part of a team
### The expected base salary ranges from $180,000\.00 \- $210,000\.00\. Salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, certifications and licenses obtained. Market and organizational factors are also considered. In addition to salary and a generous employee benefits package, successful candidates are eligible to receive a discretionary bonus.
\#LI\-Hybrid
\#LI\-NR1
Other requirements
Mizuho has in place a hybrid working program, with varying opportunities for remote work depending on the nature of the role, needs of your department, as well as local laws and regulatory obligations. Roles in some of our departments have greater in\-office requirements that will be communicated to you as part of the recruitment process.
Company Overview
Mizuho Financial Group, Inc. is the 15th largest bank in the world as measured by total assets of \~$2 trillion. Mizuho's 60,000 employees worldwide offer comprehensive financial services to clients in 35 countries and 800 offices throughout the Americas, EMEA and Asia. Mizuho Americas is a leading provider of corporate and investment banking services to clients in the US, Canada, and Latin America. Through its acquisition of Greenhill, Mizuho provides M\&A, restructuring and private capital advisory capabilities across Americas, Europe and Asia. Mizuho Americas employs approximately 3,500 professionals, and its capabilities span corporate and investment banking, capital markets, equity and fixed income sales \& trading, derivatives, FX, custody and research. Visit www.mizuhoamericas.com.
Mizuho Americas offers a competitive total rewards package.
We are an EEO/AA Employer \- M/F/Disability/Veteran.
We participate in the E\-Verify program.
We maintain a drug\-free workplace and reserve the right to require pre\- and post\-hire drug testing as permitted by applicable law.
\#LI\-MIZUHO
### About Us
Why Mizuho
Mizuho is in growth mode as we are climbing the league tables, disrupting the status quo, and attracting top talent. Positions are available across our corporate functions, and on our corporate and investment banking, capital markets, advisory, research, sales \& trading, derivatives, and financing teams.
We are looking for candidates who want to contribute to our entrepreneurial culture where people at all levels are inspired to share ideas. Our creativity sets us apart, and our perseverance drives results in creating bespoke, client\-focused solutions.
If you are interested in advancing your career working for a firm with a growth mindset and the resources of a global financial services team, we would like to hear from you.
Salary Context
This $180K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Mizuho Bank, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($195K) sits 11% below the category median. Disclosed range: $180K to $210K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Mizuho Bank AI Hiring
Mizuho Bank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $210K - $210K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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