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Work Location :
New York, New York, United States of America Hours:
40 Line of Business:
Governance & Control Pay Detail:
$170,000 - $225,000 USD TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.
As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.
Job Description:
Preferred Qualifications:
TD Securities is seeking a Director, AI Governance to lead the operation of its first-line AI Governance function. This role will play a critical leadership position within the TDS AI Office, responsible for governing the adoption and use of Artificial Intelligence across TD Securities while enabling innovation in a controlled, risk-based manner.
The Director will oversee AI governance processes including intake, review and challenge, risk assessment coordination, approvals, monitoring, issue management, and lifecycle oversight. The successful candidate will work closely with Front Office, Technology, Enterprise AI Office, Risk, Compliance, Privacy, Legal, Cybersecurity, and Internal Audit stakeholders to ensure AI use cases are implemented in alignment with internal governance requirements and evolving regulatory expectations.
This role is ideally suited to an experienced governance leader with direct AI governance experience, strong capital markets knowledge, and a proven ability to influence senior stakeholders across business and technology organizations.
Key Responsibilities
AI Governance & Oversight
- Lead the operation of the TD Securities AI Governance function.
- Coordinate governance reviews, challenge activities, risk assessments, approvals, and escalation processes.
- Ensure AI use cases satisfy governance, control, documentation, testing, and approval requirements prior to deployment.
- Maintain oversight of AI-related risks, issues, exceptions, remediation activities, and approval conditions.
Risk, Regulatory & Audit Engagement
- Monitor emerging AI governance requirements, industry developments, regulatory expectations, and best practices.
- Translate regulatory and enterprise requirements into practical governance expectations for TD Securities.
Governance Frameworks & Standards
- Maintain and evolve AI governance standards, procedures, and supporting controls.
- Support development of AI risk taxonomies, control requirements, governance metrics, and reporting.
Drive continuous improvement of AI governance processes *
Stakeholder Management & Influence
- Build strong working relationships across Front Office, Technology, Risk, Compliance, Legal, Privacy, Cybersecurity, and Audit functions.
Communicate governance risks, recommendations, and decisions clearly to senior leaders.
*
Required Qualifications
Bachelor's degree in Business, Technology, Engineering, Finance, Mathematics, or a related discipline.
Significant experience within Capital Markets, Investment Banking, or Securities markets.
Direct experience leading or operating AI Governance, Responsible AI, AI Risk, or equivalent AI oversight programs.
Experience supporting audit engagements, regulatory reviews, issue remediation programs, and evidence-based governance processes.
Strong understanding of governance operating models, approvals, oversight, challenge, escalation, and risk management practices.
Exceptional written and verbal communication skills, including interaction with senior business and technology leaders.
Preferred Qualifications
Experience governing Generative AI, Large Language Models (LLMs), agentic AI solutions, or other emerging AI technologies.
Knowledge of AI governance frameworks, Responsible AI practices, and emerging AI regulations.
Experience within Model Risk, Front Office Technology, or Quantitative Platforms.
Experience leading small teams and operating through matrixed organizations.
What Success Looks Like
Within the first 12–18 months, the successful candidate will:
- Establish themselves as a trusted AI Governance leader across TD Securities.
- Operate efficient and risk-based governance processes for AI use cases.
- Strengthen engagement between TDS and the Enterprise AI Office.
- Improve transparency, reporting, and oversight of AI-related risks and approvals.
- Support successful audit and regulatory outcomes related to AI governance.
Enable responsible AI adoption while maintaining effective governance and controls. *
- General Description
The Director Governance & Control partners with the Business and other groups globally to mitigate risk and delivers world class service to our global client base. Additionally, ensures the Business adequately consider, manage, and mitigate the risks in day-to-day activities, operations, products, and services, and is operating within risk appetite. Director Governance & Control reviews and assesses whether the Business is adhering to internal (risk management, oversight function and audit) and regulatory requirements, requests, and expectations.
Job Accountabilities:
- Fosters an effective, transparent culture of risk awareness, accountability, ethics, integrity, and conduct
- Evaluates business strategy and activities against risk appetite and provide management and stakeholders with sufficient and timely insight into the business and industry landscape
- Partners with Business to ensure risks are adequately identified, assessed, and develop risk mitigation strategies to manage current and emerging risk exposures across all risk categories and all business areas
- Supports the Business in the design, implementation, and sustainment of controls within key processes and procedures
- Delivers effective control testing and monitoring, documentation of results, and tracking execution of action plans to resolution
- In compliance with Business Change Review (BCR) Program for change initiatives delivered as projects, participates (as appropriate) in key project activities and oversight/governance committees
- In partnership with the 2nd line and oversight functions, incumbent will provide advice and facilitate discussions with management and stakeholders on relevant internal and external events and collaborate to expediently resolve incidents, identify, and track issues to remediation and develop processes to create timely, complete, and accurate reporting
- Enables proactive, data-driven, risk-based decision making
- Partners with Business to proactively prepare for internal / regulatory requests, testing engagements, exams, and reviews
- Leads all aspects of a highly complex and diverse governance and control function for an area of significant risk, complexity or scope usually involving medium to long term planning
- Ensures a coordinated approach with other business areas, the broader organization and enterprise control functions
- Leads and coordinates the overall discipline and strategy for the respective areas of Business Governance and Control while aligning to the enterprise standard methodologies
- Provides coaching, mentorship and guidance to practitioners, business, executives within area of expertise
- Strategic partner to leadership team on the management of the function, with deep industry, external / internal, enterprise knowledge, recognizing and anticipating emerging trends and identifying operational efficiencies and opportunities with other business management / enterprise areas
- Responsible to explore and deliver leading edge innovation and process improvements and build a 'learning' organization through leadership, coaching and mentorship
- Builds and cultivates an environment of innovation that leads to working smarter and simpler
- Instills an attitude of critical thinking, risk management and understanding the needs of TDS's colleagues
Responsible for the provision of a challenging and stimulating environment, primarily through delivering quality leadership and empowering the team with the skills, tools, and resources to follow through with their ideas *
Education & Experience
- Undergraduate degree preferred
- 10 + years related experience required
- Knowledge of the regulatory environment and requirements
- Build strong relationship with business partners and provide challenge and push back as needed
- Proven ability to successfully lead change and manage effectively in a matrixed environment
- Skillful communicator and influencer to build relationships, advance decision making, and effect change over time
- Highly motivated individual with a track record successfully implementing large scale globally sourced programs with superior results.
- Ability to work with a pool of experts and ensure high levels of service even when the effort crosses organizational boundaries
Thrives in a fast-paced environment, managing multiple deadlines and priorities *
Who We Are
TD Securities offers a wide range of capital markets products and services to corporate, government, and institutional clients who choose us for our innovation, execution, and experience. With more than 6,500 professionals operating out of 40 cities across the globe, we strive to make every interaction, product and experience remarkably human and refreshingly simple. Our services include underwriting and distributing new issues, providing trusted advice and industry-leading insight, extending access to global markets, and delivering integrated transaction banking solutions. In 2023, we acquired Cowen Inc., offering our clients access to a premier U.S. equities business and highly-diverse equity research franchise, while growing our strong, diversified investment bank.
Together, we are reimagining what banking can be for our clients, colleagues and communities.
Our Total Rewards Package
Our Total Rewards package reflects the investments we make in our colleagues to help them and their families achieve their financial, physical and mental well-being goals. Total Rewards at TD includes base salary and variable compensation/incentive awards (e.g., eligibility for cash and/or equity incentive awards, generally through participation in an incentive plan) and several other key plans such as health and well-being benefits, savings and retirement programs, paid time off (including Vacation PTO, Flex PTO, and Holiday PTO), banking benefits and discounts, career development, and reward and recognition. Learn more
Additional Information:
We’re delighted that you’re considering building a career with TD. Through regular development conversations, training programs, and a competitive benefits plan, we’re committed to providing the support our colleagues need to thrive both at work and at home.
Colleague Development
If you’re interested in a specific career path or are looking to build certain skills, we want to help you succeed. You’ll have regular career, development, and performance conversations with your manager, as well as access to an online learning platform and a variety of mentoring programs to help you unlock future opportunities.
If you’re passionate about helping clients and building deep, lasting relationships, TD offers diverse career paths where you can grow your expertise and make a meaningful impact.
We're committed to your success and foster a respectful workplace where diverse perspectives are valued, everyone has fair opportunities to grow, and you can unlock your full potential to achieve your career goals. Here at TD, we hire and develop the best.
Training & Onboarding
We will provide training and onboarding sessions to ensure that you’ve got everything you need to succeed in your new role.
Interview Process
We’ll reach out to candidates of interest to schedule an interview. We do our best to communicate outcomes to all applicants by email or phone call.
Accommodation
TD Bank is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, status as a protected veteran or any other characteristic protected under applicable federal, state, or local law.
If you are an applicant with a disability and need accommodations to complete the application process, please email TD Bank US Workplace Accommodations Program at [email protected] . Include your full name, best way to reach you and the accommodation needed to assist you with the applicant process.
Salary Context
This $170K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At TD, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($197K) sits 7% below the category median. Disclosed range: $170K to $225K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
TD AI Hiring
TD has 7 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Mount Laurel, NJ, US, New York, NY, US, New York, NY. Compensation range: $125K - $225K.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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