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We’re building a relationship-oriented bank for the modern world. We need talented, passionate professionals who are dedicated to doing what’s right for our clients.
At CIBC, we embrace your strengths and your ambitions, so you are empowered at work. Our team members have what they need to make a meaningful impact and are truly valued for who they are and what they contribute.
To learn more about CIBC, please visit CIBC.com
The Head of AI Underwriting Products is a senior leader responsible for product ownership of designated AI-enabled lending products, helping to lead lending platform transformation initiatives, and the development of a forward-looking lending product strategy over time.
The role is accountable for defining product vision and priorities, driving modernization of lending capabilities and processes, and ensuring alignment across business, technology, operations, risk, and control functions. The position applies strategic thinking, strong product management discipline, and change leadership to deliver improved client outcomes, operational efficiency, platform scalability, and business growth. The role requires substantial communication, stakeholder management, and problem-solving skills to translate business priorities into practical roadmaps and measurable results.
KEY ACCOUNTABILITIES
- Own the vision, roadmap, and business outcomes for assigned lending technology products that incorporate AI and automation to improve speed to decisions and enhance risk-management.
- Act as senior product owner for designated lending capabilities, ensuring priorities align to business strategy, client needs, risk requirements, and operational realities.
- Assist in leading the development of product frameworks, strategy, organization structure and governance.
- Play a key role in the transformation of the lending platform, including modernization of processes, capabilities, workflows, and supporting tools.
- Align with and influence the global enterprise processes, products, and approaches where appropriate.
- Assess AI-enabled product and platform changes for impacts to client experience, operational processes, controls, risk, and regulatory obligations.
- Evaluate and attract talent to assist in the development of lending products, over time leading a team to deliver on business priorities and outcomes.
- Develop and refine the medium and long-term strategy for the US lending product portfolio, including product evolution, innovation opportunities, and investment priorities.
- Partner with technology teams to ensure product and platform changes are delivered in line with business priorities and target-state architecture.
- Collaborate with operations, risk, compliance, legal, finance, and frontline partners to ensure solutions are practical, controlled, and aligned with enterprise objectives.
- Drive decision-making and trade-off discussions across scope, timing, value, and risk in a complex, matrixed environment.
- Ensure products and related platform changes operate within applicable policy, regulatory, and risk management frameworks.
- Stay informed on market trends, competitor developments, client needs, and emerging lending capabilities to inform strategic direction.
CROSS-FUNCTIONAL RELATIONSHIPS
- Technology
- Operations
- Risk Management
- Compliance
- Legal
- Finance
- Sales and Distribution
- Product Management
- Enterprise Strategy
- Data and Analytics
- Frontline business leaders and functional partners
KNOWLEDGE AND SKILLS
- University degree in business, finance, commerce, engineering, or a related field.
- MBA or other advanced degree considered an asset.
- Typically 10+ years of progressive experience in lending, product management, transformation, strategy, or related financial services leadership roles.
- Strong knowledge of lending products, underwriting process and priorities, the lending lifecycle, operations, controls, and enabling technologies.
- Demonstrated experience in product ownership, product roadmap development, and strategic transformation delivery.
- Cross board collaboration and relationship building experience.
- Familiarity with emergent AI capabilities and how they are being leveraged at financial institutions.
- Strategic thinking and the ability to develop a clear product and platform vision linked to business priorities, latest technological advances, and measurable outcomes.
- Transformation leadership and the ability to lead large, cross-functional initiatives involving process, technology, and operating model change.
- Strong written and oral communication skills, including preparation of business cases, executive presentations, and roadmap materials.
- Interpersonal effectiveness, influencing ability, and relationship management across business, technology, operations, and control functions.
- Client-focused mindset, commercial and financial acumen, risk and control mindset, and strong execution discipline.
COMPLIANCE REQUIREMENTS/RESPONSIBILITIES
- As an employee of CIBC, the incumbent must comply with all applicable CIBC and Line of Business policies, standards, guidelines and controls.
AUTHORITIES/DECISION RIGHTS
- Recommend product roadmap priorities and changes to lending processes, capabilities, and platform features.
- Influence investment decisions and strategic priorities related to lending product development and platform transformation.
- Escalate risks, issues, and interdependencies affecting roadmap delivery or product performance.
- Make recommendations on business requirements, prioritization, and target-state operating approaches to enhance client outcomes and operational effectiveness.
CONDUCT & CULTURE RISK
Our CIBC risk culture is based on employees striving to exceed the expectations of ourselves and our leadership’s identification and mitigation of risks in their daily responsibilities, not just in quarterly or annual monitoring/assessments. We all are accountable for managing risk.
As an employee of CIBC the incumbent must conduct themselves (and foster an environment for others) in a manner consistent with our strong risk culture. This includes:
- Following all aspects of the CIBC Code of Conduct, as well as all applicable CIBC policies, frameworks, guidelines, processes and controls
- At all times acting in accordance with our Purpose and shared values, to achieve our Bank’s strategic goals
- Understanding and following the qualitative and quantitative components of our Risk Appetite Statements
- Completing all annual Corporate Mandatory Training and Testing modules, as well as any additional business-specific modules, as required and employing the learnings in daily activities and undertakings
- Escalating matters through one of the appropriate channels identified in the CIBC Code of Conduct (i.e., HR, management, Ethics Hotline, Whistleblower, etc.) upon observing activities that may be inconsistent with CIBC’s policies, frameworks, guidelines, processes and controls
- Speaking up if witnessing behaviors that drive poor or unfair outcomes for clients, team members or other stakeholders
Escalating matters that can result in adverse market practices and outcomes, thereby negatively impacting CIBC’s reputation as a leading financial institution.
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WORKING CONDITIONS
- This role operates within a normal office environment in Chicago.
Travel required: Occasional
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At CIBC, we offer a competitive total rewards package. This role has an expected salary range of $225,000- $300,000 based on experience, qualifications, and location of the position. The successful candidate may be eligible to participate in the relevant business unit’s incentive compensation plan, which also may include a discretionary bonus component. CIBC offers a full range of benefits and programs to meet our employee’s needs; including Medical, Dental, Vision, Health Savings Account, Life Insurance, Disability, and Other Insurance Plans, Paid Time Off (including Sick Leave, Parental Leave, and Vacation), Holidays, and 401(k), in addition to other special perks reserved for our team members.
This position does not offer visa sponsorship.
What CIBC Offers
At CIBC, your goals are a priority. We start with your strengths and ambitions as an employee and strive to create opportunities to tap into your potential. We aspire to give you a career, rather than just a paycheck.
- We work to recognize you in meaningful, personalized ways including a competitive salary, incentive pay, banking benefits, a benefits program*, a vacation offering, wellbeing support, and MomentMakers, our social, points-based recognition program.
- Our spaces and technological toolkit will make it simple to bring together great minds to create innovative solutions that make a difference for our clients.
- We cultivate a culture where you can express your ambition through initiatives like Purpose Day; a paid day off dedicated for you to use to invest in your growth and development.
- Subject to plan and program terms and conditions
What you need to know
- CIBC is committed to creating an inclusive environment where all team members and clients feel like they belong. We seek applicants with a wide range of abilities and we provide an accessible candidate experience. If you need accommodation, please contact [email protected]
- You need to be legally eligible to work at the location(s) specified above and, where applicable, must have a valid work or study permit .
- We may ask you to complete an attribute-based assessment and other skills tests (such as simulation, coding, MS Office). Our goal for the application process is to get to know more about you, all that you have to offer, and give you the opportunity to learn more about us.
Salary Context
This $225K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CIBC, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($262K) sits 24% above the category median. Disclosed range: $225K to $300K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
CIBC AI Hiring
CIBC has 2 open AI roles right now. They're hiring across AI Safety, AI/ML Engineer. Positions span Toronto, Ontario, Canada, Chicago, IL, US. Compensation range: $300K - $300K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% below 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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