Interested in this AI/ML Engineer role at TD?
Apply Now →Skills & Technologies
About This Role
Work Location:
New York, New York, United States of America
Hours:
40
Pay Details:
$123,050 \- $201,170 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.
Line of Business:
Analytics, Insights, \& Artificial Intelligence
Job Description:
ATTENTION:
This role is not eligible for TD work visa support or sponsorship (e.g., H\-1B, F\-1 OPT/STEM OPT, TN or other work visa authorizations). Applicants must be either a US Citizen or have a US Resident Green Card authorization, without current or future need for TD sponsorship .
Job Description:
The Senior Manager, Data Science leads a specialized team of data professionals varying in size and complexity that are responsible for aiding to drive changes and improvement in business practices through data science. This role manages the overall data scientist team or function for a key business which may include Modelers and/or Data Scientist roles. This role may also oversee the development of data models, data mining and analytic solutions.
Department Overview:
The US Financial Crime Risk Modeling \& Advanced Analytics team within US Financial Crime department is responsible for developing, maintaining, and enhancing the Enterprise Anti\-Money Laundering / Counter\-Terrorism Financing (AML/CTF) models/AI solutions to comply with regulatory requirements/changes and internal policies, support TD's global AML/CTF strategies, and address emerging risks, in accordance with best industry practice.
We are seeking a Data Science Senior Manager to join us to innovate, drive, and support innovation initiatives and business as usual operations in across the FCRM areas including, but not limited to, customer rating, sanctions screening, transaction monitoring, emerging risk, model performance monitoring, analytics, and reporting. This role will lead development of tools to improve risk detection of complex areas and enhance process efficiency in business operations across FCRM, leveraging the latest tools such as LLMs. They will also own interaction with key stakeholders and any applicable model lifecycle activities.
Depth \& Scope:
- Provides people management leadership by hiring the best talent, setting goals, developing staff, managing employee performance and compensation decisions, promoting teamwork and handling any/all disciplinary actions, as required
- Oversees and leads a large and/or highly complex and diverse analytical function for an area of significant risk, complexity or scope
- Strategic partner to leadership team on the management of the portfolio and financials, with deep industry, external/internal, enterprise knowledge, recognizing and anticipating emerging trends and identifying operational efficiencies and opportunities with other business management/enterprise areas
- Facilitates key strategic discussions and provides thought leadership to executive audience (output may include strategic roadmap and/or deliverables/frameworks/short to long term goals etc.)
- Sets operational team direction and collaborates with others to execute on common goals
- Focuses on longer range planning for functional area (e.g. 12 months or greater)
Education \& Experience:
- Undergraduate degree or advanced technical degree preferred (e.g., math, physics, engineering, finance or computer science) Graduate's degree preferred with either progressive project work experience, or;
- 7\+ year of relevant experience; higher degree education and research tenure can be counted
Preferred Skills:
- Minimum of 7 years of relevant experience required; candidates with 5 years of highly relevant experience may also be considered.
- Demonstrated experience leading data science, analytics, or innovation initiatives for enterprise\-scale solutions in production environments.
- Proficiency in Python, SQL, or equivalent programming languages.
- Experience in financial crimes, compliance risk analytics, model risk, or related risk management domains.
- Strong understanding of large language models (LLMs), machine learning, and quantitative risk analysis.
- Experience optimizing, testing, and tuning AML/CTF solutions.
- Experience developing dashboards, web applications, or other user\-facing analytical tools.
- Proven experience managing teams and collaborating effectively across cross\-functional stakeholders.
Customer Accountabilities:
- Leads team of Data Scientists and provides day\-to\-day direction as needed
- Acts as People Manager and is responsible for ongoing coaching and development, setting objectives, assessing performance
- Works closely with business owners to identify opportunities and serves as an ambassador for data science
- Leads and oversees the design and delivery of enterprise analytic solutions for customers
- Works in a highly interactive, team\-oriented environment with Big Data developers, and analytical experts
- Collaborates with business partners to shape and prioritize ad hoc analysis
Shareholder Accountabilities:
- Provides analytical thought leadership and stays current on developments in data mining and the application of data science
- Manages workload of data science team; assigning data request to staff based on skills and development needs
- Supports execution with excellence on key initiatives/programs
- Designs effective test and learns for various programs or scenarios
- Develops business specific plans; ensures work and resources are aligned to support objectives
- Identifies opportunities for business growth within a specific business or function by identifying potential use cases and value drivers
- Proactively supports the identification of issues, trends and opportunities, and brings forward recommendations based on judgment and facts
- Leads team to prepare framework to succinctly take complex data and translate it into clear and concise recommendations
- Ensures deep understanding and contributes to the achievement of the business strategy, goals, and objectives
- Ensures team adheres to enterprise frameworks and methodologies related to overall business management activities
- Leads relationships with corporate and/or control functions to ensure alignment with enterprise and/or regulatory requirements
- Supports team in staying knowledgeable on emerging issues, trends and evolving regulatory requirements and assesses potential impacts to the Bank
- Assesses/identifies key issues and escalates to appropriate levels and relevant stakeholders and business management where required
- Identifies, mitigates and reports on risk issues per enterprise policy/guidance and ensures appropriate escalation processes are followed
- Ensures business operations are in compliance with applicable internal and external requirements (e.g. financial controls, segregation of duties, transaction approvals and physical control of assets)
- Leads relationships with business lines / corporate and/or control functions to ensure alignment with enterprise and/or regulatory requirements
- Leads or contributes to cross\-functional/enterprise initiatives as an organizational or subject matter expert helping to identify risk/provide guidance for complex situations
- Protects the interests of the organization – identifies and manages risks, and escalates non\-standard, high\-risk transactions/activities as necessary
- Manages oversight process, risk\-based identification and monitoring of related risks and regulatory compliance across the supported functions, while ensuring key controls and processes are effectively managed
- Oversees or leads the facilitation and/or implementation of action/remediation plans to address performance/risk/governance issues
- Keeps abreast of emerging issues, trends, and evolving regulatory requirements and assesses potential impacts
- Maintains a culture of risk management and control, supported by effective processes in alignment with risk appetite
Employee/Team Accountabilities:
- Cultivates and models the Colleague Promise to support colleague growth, and a culture of care; makes an impact at work and in our communities by leading with authenticity and supporting well\-being to represent TD's brand
- Connects the alignment of colleague's contributions with the TD Shared Commitments
- Builds and retains an engaged and diverse team that embraces diversity of thought, creativity and curiosity; where every colleague and customer are valued, respected, and listened to; committed to a common goal and collaborates to move with speed and get things done
- Demonstrates inclusive leadership by taking meaningful action with intention to support colleagues and customers across all dimensions of diversity, including those from underrepresented communities, being actively anti\-racist, attracting and retaining diverse slate of candidates, nurturing mutual respect, inclusivity of thought and collaboration to drive successful results
- Sustains, identifies strong talent, recruits and develops a diverse talent pipeline of qualified workforce to innovate and maximize individual strengths to lead to a better business outcome
- Enables colleague growth by encouraging colleague development to achieve career and business objectives, ensuring timely feedback, motivating appreciation and recognition to all colleagues
- Enables a continuous learning culture by proactively seeking, listening to and actioning feedback from peers and from colleague listening opportunities to continuously improve the colleague experience and grow your personal leadership
- Fosters an environment that promotes sharing of knowledge, information, skills, and subject matter expertise among the team; ensures timely management and escalation of issues and creates opportunities to collaborate with other functions and team
- Leads team through change and creates an environment where teams feel psychologically safe to challenge current practices by modeling resiliency and flexibility, communicating a compelling vision with clarity and empowering colleagues to drive innovation
- Contributes to the development of business segment and/or enterprise functional strategic priorities within their operational area or field of specialty that drive results
- Develops annual and/or long\-term plans for own area that are aligned with enterprise\-wide priorities, reinforces a focus on results that align to One TD
- Fosters a high\-performance culture by setting team targets and objectives, promotes and facilitates on\-going feedback/coaching and conducting Quarterly Check\-Ins for all colleagues to drive accountability and business results
- Manages employees in compliance with all human resources policies, procedures and guidelines of conduct
Physical Requirements:
Never: 0%; Occasional: 1\-33%; Frequent: 34\-66%; Continuous: 67\-100%
- Domestic Travel – Occasional
- International Travel – Never
- Performing sedentary work – Continuous
- Performing multiple tasks – Continuous
- Operating standard office equipment \- Continuous
- Responding quickly to sounds – Occasional
- Sitting – Continuous
- Standing – Occasional
- Walking – Occasional
- Moving safely in confined spaces – Occasional
- Lifting/Carrying (under 25 lbs.) – Occasional
- Lifting/Carrying (over 25 lbs.) – Never
- Squatting – Occasional
- Bending – Occasional
- Kneeling – Never
- Crawling – Never
- Climbing – Never
- Reaching overhead – Never
- Reaching forward – Occasional
- Pushing – Never
- Pulling – Never
- Twisting – Never
- Concentrating for long periods of time – Continuous
- Applying common sense to deal with problems involving standardized situations – Continuous
- Reading, writing and comprehending instructions – Continuous
- Adding, subtracting, multiplying and dividing – Continuous
The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilities, duties and skills required. The listed or specified responsibilities \& duties are considered essential functions for ADA purposes.
\#LI\_AMCBCorporate
Who We Are:
TD is one of the world's leading global financial institutions and is the fifth largest bank in North America by branches/stores. Every day, we strive to make every interaction, product, and experience remarkably human and refreshingly simple for over 27 million households and businesses in Canada, the United States and around the world. More than 95,000 TD colleagues bring their skills, talent, and creativity to foster deeper relationships, ensure disciplined execution, and build a simpler, faster banking experience. TD is deeply committed to being a leader in client experience, that is why we believe that all colleagues, no matter where they work, are client facing. 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 $123K-$201K range is below 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 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($162K) sits 26% below the category median. Disclosed range: $123K to $201K.
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.
TD AI Hiring
TD has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Mount Laurel, NJ, US, New York, NY, US. Compensation range: $125K - $280K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.