Interested in this AI/ML Engineer role at TD?
Apply Now →About This Role
Work Location :
New York, New York, United States of America
Hours:
40
Line of Business:
TD Securities
Pay Detail:
$115,000 \- $200,000 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:
Provides market research support in compiling and publishing high quality, comprehensive analysis and research on individual corporations, industries, financial markets, and economic developments as assigned.
Depth \& Scope:
- Expert level professional role requiring in\-depth knowledge/expertise in own domain/field of specialty and working knowledge of broader related areas to run data analysis and develop research reports
- Integrates the broader organizational and industry context into advice and solutions
- Solves complex problems requiring analysis of multiple variables, which may require consultation with multiple stakeholders
- Uses advanced methods to contribute to new solutions and recommend standards against which others will operate
- Interprets guidelines, standards, policies, and results of analysis to inform decision making at senior levels
- Builds stakeholder alignment in leading activities, identifying, and leading problem resolution
- Works independently as a subject matter expert within own area of specialty and act as an escalation point and/or knowledge resource for others
Education \& Experience:
- Undergraduate degree
- FINRA SIE, Series 63, 86, \& 87 or willing to obtain
- Holds or is working towards a CFA designation preferred for publishing analysts and associates
- Masters or PhD preferred
- 2\+ years related experience
Customer Accountabilities:
- Compiles, analyzes, and evaluates data, conducting modeling on multiple data inputs related to assigned:
+ companies/industries and/or
+ financial markets and economic analysis research reports (e.g., initiating coverage reports, thematic reports, quarterly reporting initiatives, and marketing packages)
- Assists in the development of valuation tools and provides advice to institutional clients
- Assists in the preparation of forecasting models (e.g., forecast quarterly and annual earnings)
- Maintains and enhances financial models and databases
- Stays current on market data, economic information, and key industry developments as well as internal policies and regulatory standards (as communicated by Compliance) to assist in maximizing profitability through sound and well\-informed decisions
- Develops and maintains contact with market participants
- Works closely with senior research leads on the ongoing publication of research reports and may assist with ad hoc projects as assigned
- May have interaction with clients; develops and presents client presentations
Shareholder Accountabilities:
- Keeps abreast of market data, economic information, and key industry developments, as well as internal policies and regulatory standards (as communicated by Compliance) at all times to assist in maximizing profitability through sound and well\-informed decisions; develop and maintain contacts with market participants
- Demonstrates governance, control, and risk management behaviors in alignment with TD policies and practices
- Adheres to enterprise frameworks or methodologies that relate to activities for our business area
- Ensures respective programs/policies/practices are well managed, meets business needs, complies with internal and external requirements, and aligns with business priorities
- Consistently exercises discretion in managing correspondence, information, and all matters of confidentiality; escalate issues where appropriate
- Participates in cross\-functional/enterprise initiatives as a subject matter expert helping to identify risk/provide guidance for complex situations
- Conducts internal and external research projects; support the development/delivery of presentations and communications to management or broader audience
- Conducts meaningful analysis at the functional or enterprise level using results to draw conclusions, make recommendations, assess the effectiveness of programs/policies/practices
- Monitors service, productivity, and assess efficiency levels within own function and implement continuous process/performance improvements where opportunities exist
- Actively manages relationships within and across various business lines, corporate, and/or control functions and ensures alignment with enterprise and/or regulatory requirements
- Maintains a culture of risk management and control, supported by effective processes in alignment with risk appetite
Employee/Team Accountabilities:
- Participates fully as a member of the team, support a positive work environment that promotes service to the business, quality, innovation, and teamwork, and ensure timely communication of issues/points of interest
- Provides thought leadership and/or industry knowledge for own area of expertise and participate in knowledge transfer within the team and business unit
- Keeps current on emerging trends/developments and grow knowledge of the business, related tools, and techniques
- Participates in personal performance management and development activities, including cross training within own team
- Keeps others informed and up to date about the status/progress of projects and/or all relevant or useful information related to day\-to\-day activities
- Contributes to team development of skills and capabilities through mentorship of others, by sharing knowledge and experiences and leveraging best practices
- Leads, motivates, and develops relationships with internal and external business partners/stakeholders to develop productive working relationships
- Contributes to a fair, positive, and equitable environment that supports a diverse workforce
- Acts as a brand ambassador for your business area/function and the bank, both internally and/or externally
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.
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 $115K-$200K range is below the median 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 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($157K) sits 27% below the category median. Disclosed range: $115K to $200K.
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.
TD AI Hiring
TD has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $155K - $200K.
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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