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About This Role
Work Location:
New York, New York, United States of America
Hours:
40
Pay Details:
$96,130 \- $155,950 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:
The Applied Machine Learning Scientist II is responsible for providing technical knowledge and expertise on advanced analytics and machine learning across a broad range of analytics functions including data and modelling frameworks, tools, technology, processes and procedures.
This role generally provides expertise in stakeholder interactions related to complex advanced analytics related material. Additionally, this role plays a lead role in the development of AI/ML systems to solve a range of complex problems and is adept at translating business objectives into technical solutions.
Department Overview:
The Advanced Analytics (AA) team at TD Bank serves as a Center of Excellence (CoE) delivering advanced analytics, Artificial Intelligence (AI), and Machine Learning (ML) solutions across U.S. business lines. The team partners closely with fraud, risk, operations, digital, and enterprise stakeholders to solve complex business challenges through data\-driven innovation.
AA is at the forefront of developing scalable AI capabilities that improve operational efficiency, strengthen fraud and risk management, and enhance customer experiences. The team leverages modern cloud\-based technologies and advanced AI methodologies — including Generative AI, Agentic AI systems, machine learning, graph analytics, NLP, and predictive modeling — to build intelligent solutions that create measurable business impact.
The organization fosters a highly collaborative and innovative environment where scientists work closely with business leaders, engineers, MLOps, governance teams, and enterprise AI partners to transform emerging AI technologies into production\-ready enterprise solutions.
Position Overview:
We are seeking a highly experienced and technically strong Applied Machine Learning Scientist to lead the development of next\-generation AI/ML solutions focused on fraud, risk, operational intelligence, and decision optimization.
This role is ideal for a senior AI practitioner who combines deep technical expertise with strong business acumen and the ability to lead complex cross\-functional initiatives from concept through production deployment. The successful candidate will play a key role in advancing the organization’s capabilities in Generative AI, Agentic AI, machine learning, and intelligent automation.
The role requires hands\-on expertise in building scalable AI systems while also serving as a technical leader and mentor for junior scientists. Candidates should be comfortable operating in highly ambiguous problem spaces, rapidly prototyping innovative solutions, and collaborating directly with senior business stakeholders to translate strategic priorities into deployable AI products.
Key Responsibilities include:
- Lead the end\-to\-end development and deployment of advanced AI/ML solutions addressing strategic business challenges across fraud, risk, and operational domains.
- Design and implement production\-grade machine learning systems using advanced statistical modeling, deep learning, Generative AI, NLP, graph analytics, and Agentic AI frameworks.
Drive innovation in emerging AI capabilities, including:
\*LLM\-powered applications
\*AI copilots and agentic workflows
\*Retrieval\-Augmented Generation (RAG)
\*Multi\-agent orchestration frameworks
\*Intelligent decision support systems
- \* Human\-in\-the\-loop AI solutions
- Develop scalable data science and AI pipelines leveraging technologies such as Python, Databricks, Azure, PySpark, MLflow, vector databases, orchestration frameworks, and modern AI tooling ecosystems.
- Partner closely with business leaders, fraud strategy teams, engineering, MLOps, governance, and enterprise AI organizations to identify opportunities and deliver measurable business value.
- Translate ambiguous business problems into analytical frameworks, technical solutions, and actionable insights.
- Lead technical architecture discussions and contribute to AI platform strategy, solution design, and enterprise AI standards.
- Communicate complex analytical concepts and AI solution designs effectively to executive leadership, business stakeholders, and governance partners.
- Ensure strong model governance, explainability, monitoring, and responsible AI practices throughout the AI/ML lifecycle.
- Mentor and guide junior scientists by promoting best practices in machine learning, software engineering, experimentation, and AI product development.
- Maintain awareness of emerging industry trends, academic research, and evolving AI technologies, proactively identifying opportunities to apply them within the organization.
Depth \& Scope:
- Adept at technical project execution, strategic planning and effective communication. Accountable for specialized knowledge in a field of AI/ML
- Works autonomously and accountable for acting as a lead within a function
- Undertakes and completes a variety of complex initiatives requiring seasoned specialist AI/ML knowledge and/or the integration of cross functional processes
- Typically deals with senior/executive management
Education \& Experience:
- Undergraduate degree required, advanced technical degree preferred (e.g., math, physics, engineering, finance or computer science) Graduate's degree preferred with either progressive project work experience, or;
- 1\+ years relevant experience (includes post graduate experience)
Preferred Qualifications:
- Advanced degree (Master’s or PhD preferred) in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or related quantitative discipline.
- Extensive experience developing and deploying advanced AI/ML solutions in enterprise environments.
- Strong hands\-on experience with machine learning, deep learning, NLP, LLMs, Generative AI, and modern AI application architectures.
- Experience building or implementing:
+ Agentic AI systems
+ AI copilots
+ Retrieval\-Augmented Generation (RAG)
+ Prompt engineering frameworks
+ Multi\-agent workflows
+ Conversational AI solutions
+ Knowledge retrieval systems
- Deep expertise in Python, PySpark, SQL, and modern ML/AI frameworks such as PyTorch, TensorFlow, LangChain, LangGraph, Hugging Face, MLflow, or equivalent ecosystems.
- Experience working with cloud\-based AI/ML platforms such as Azure Databricks and distributed computing environments.
- Strong software engineering and productionization skills, including Git\-based development workflows, CI/CD concepts, API integration, and scalable AI solution deployment.
- Experience developing AI/ML solutions within fraud, financial services, risk, payments, or highly regulated industries is strongly preferred.
- Strong understanding of AI governance, explainability, model risk management, and responsible AI principles.
- Demonstrated ability to lead complex initiatives and influence cross\-functional stakeholders in fast\-paced enterprise environments.
- Excellent communication and presentation skills with the ability to explain technical concepts to both technical and non\-technical audiences.
- Proven ability to mentor junior team members and contribute to a strong collaborative team culture.
- Strong research mindset with the ability to evaluate and operationalize emerging AI techniques from academic and industry research.
Customer Accountabilities:
- Provides strong knowledge in a given AI/ML area. Delivers insights into leading analytic practices, designs and leads iterative learning and development cycles, and ultimately produces new and creative analytic solutions that will become core deliverables
- Works closely with business owners to identify opportunities and serves as an ambassador for data science
- Designs and delivers enterprise analytic solutions for customers
- Develops powerful business insights from a broad range of data using advanced machine learning techniques
- Works in a highly interactive, team\-oriented environment with Big Data developers, engineers, modelers and others
Shareholder Accountabilities:
- Adheres to enterprise frameworks or methodologies that relates 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; escalates issues where appropriate
- 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)
- 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; supports the development/delivery of presentations/communications to management or broader audience
- Conducts meaningful analysis at the functional or enterprise level using results to draw conclusions, makes recommendations, assesses the effectiveness of programs/policies/practices
- Monitors service, productivity and assesses efficiency levels within own function and implements continuous process/performance improvements where opportunities exist
- Leads/facilitates and/or implements action/remediation plans to address performance/risk/governance issues
- Actively manages relationships within and across various business lines, corporate and/or control functions and ensures alignment with enterprise and/or regulatory requirements
- 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:
- Participates fully as a member of the team, supports a positive work environment that promotes service to the business, quality, innovation and teamwork and ensures timely communication of issues/points of interest
- Provides thought leadership and/or industry knowledge for own area of expertise in own area and participates in knowledge transfer within the team and business unit
- Keeps current on emerging trends/developments and grows 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 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 $96K-$155K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($126K) sits 30% below the category median. Disclosed range: $96K to $155K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
TD AI Hiring
TD has 3 open AI roles right now. They're hiring across AI Software Engineer, Data Scientist, AI/ML Engineer. Positions span Mount Laurel, NJ, US, New York, NY, US. Compensation range: $155K - $159K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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