Interested in this AI/ML Engineer role at Ally Financial?
Apply Now →About This Role
Ally and Your Career
Ally Financial only succeeds when its people do - and that’s more than some cliché people put on job postings. We love this stuff! We see our people as, well, people - with interests, families, friends, dreams, and causes that are all important to them. Our focus is on the health and safety of our teammates as well as work-life balance and diversity and inclusion. From generous benefits to a variety of employee resource groups, we strive to build paths that encourage employees to stretch themselves professionally. We want to help you grow, develop, and learn new things. You’re constantly evolving, so shouldn’t your opportunities be, too?
Work Schedule : Ally designates roles as (1) fully on-site, (2) hybrid, or (3) fully remote. Hybrid roles are generally expected to be in the office a certain number of days per week as indicated by your manager. Your hiring manager will discuss this role's specific work requirements with you during the hiring process. All work requirements are subject to change at any time based on leader discretion and/or business need.
The Opportunity
At Ally, we believe Data & AI will define the next generation of customer experiences, operational excellence, and business innovation. We're seeking a visionary Senior Director of Applied Data & AI to lead a multidisciplinary organization focused on transforming Ally's data, analytics, AI, and automation capabilities into measurable business outcomes.
This leader will operate at the intersection of technology, business, and innovation, partnering closely with business executives, technology leaders, and the Data & AI Platforms organization to deliver scalable solutions that solve critical business challenges and accelerate value creation across the enterprise.
The Applied Data & AI organization serves as the bridge between Ally's Data & AI platform capabilities and business outcomes, applying modern data, AI, analytics, and software engineering practices to create innovative products, intelligent solutions, and operational efficiencies at scale.
This role is ideal for a leader who thrives on building high-performing teams, influencing enterprise strategy, and leveraging technology to transform how organizations operate and compete. The Work Itself
- Lead Ally's Applied Data & AI engineering organization, responsible for delivering business-facing data, analytics, AI, and intelligent automation solutions across the enterprise.
- Partner with business and technology leaders to identify high-value opportunities and translate them into scalable, production-ready capabilities that drive measurable outcomes.
- Work closely with the Senior Director of Data & AI Platforms to leverage, extend, and influence the evolution of Ally's core data and AI platforms, ensuring investments align with current and emerging business needs.
- Lead the delivery of AI-powered products and intelligent solutions that enhance customer experiences, improve operational efficiency, reduce risk, and accelerate growth.
- Serve as a trusted strategic advisor to senior leaders, helping shape technology roadmaps, investment priorities, and enterprise Data & AI adoption strategies.
- Influence enterprise technology strategy and help define the future direction of Ally's Data & AI capabilities.
- Champion modern engineering practices, including cloud-native architectures, platform engineering, API-first development, AI-enabled software engineering, automation, and DevSecOps.
- Foster deep partnerships across product, engineering, data science, analytics, risk, operations, and business teams to deliver solutions that create meaningful business impact.
- Build, inspire, and develop a world-class organization of engineers, architects, and technical leaders while fostering an inclusive, high-performance culture.
The Qualifications We’re Looking For
The successful candidate is a technology executive with a proven track record of leading large-scale engineering organizations and delivering innovative solutions that create measurable business value.
What You Bring
- Significant technical experience leading data, AI, and software engineering organizations in complex enterprise environments.
- Demonstrated success partnering with business leaders to deliver technology solutions that drive revenue growth, operational efficiency, customer satisfaction, or risk reduction.
- Deep expertise across modern Data & AI ecosystems, including cloud platforms, data engineering, analytics, APIs, machine learning, generative AI, intelligent applications, and automation technologies.
- Demonstrated ability to connect business strategy, data assets, AI capabilities, and engineering execution into cohesive solutions that deliver measurable outcomes.
- Exceptional collaboration, communication, and influence skills, with the ability to align diverse stakeholders around a shared vision, strategy, and roadmap.
- Strategic and future-oriented mindset with the ability to challenge the status quo, anticipate emerging opportunities, and work backward from long-term business goals to deliver transformational solutions.
- A people-first leadership approach that balances ambition, accountability, curiosity, and humility while building high-performing teams.
- Proven success developing technical leaders and creating inclusive, high-performing organizations that attract and retain top talent.
- Experience leading organizational transformation and driving enterprise adoption of emerging technologies, including AI and automation.
- Experience operating in highly regulated, complex environments where technology innovation must be balanced with risk management, security, and governance.
- Advanced degree preferred.
Minimum Qualifications
- 12+ years of relevant experience or equivalent combination of education and experience
- High school Diploma or GED equivalent
How We'll Have Your Back
Ally's compensation program offers market-competitive base pay and pay-for-performance incentives (bonuses) based on achieving personal and company goals. Our Total Rewards program includes industry-leading compensation and benefits plus additional incentives that are designed to meet your needs and those of your family so you can get the most out of your career and your life, including:
Time Away: Program starts at 20 paid time off days in addition to 11 paid holidays and 8 hours of volunteer time off yearly (time off days are prorated based on start date and program varies based on full or part-time status and management level).
Planning for the Future: plan for the near and long term with an industry-leading 401K retirement savings plan with matching and company contributions, student loan pay downs and 529 educational save up assistance programs, tuition reimbursement, employee stock purchase plan, and financial learning center and financial coach access.
Supporting your Health & Well-being: flexible health and insurance options including medical, dental and vision, employee, spouse and child life insurance, short- and long-term disability, pre-tax Health Savings Account with employer contributions, Healthcare FSA, critical illness, accident & hospital indemnity insurance, and a total well-being program that helps you and your family stay on track physically, socially, emotionally, and financially.
Building a Family: adoption, surrogacy and fertility assistance as well as paid parental and caregiver leave, Dependent Day Care FSA back-up child and adult/elder care days and childcare discounts.
Work-Life Integration: other benefits including Mentally Fit Employee Assistance Program, subsidized and discounted Weight Watchers® program and other employee discount programs.
Other Compensations: depending on the role for which you are considered, you may be eligible for travel allowances, relocation assistance, a signing bonus and/or equity.
To view more detailed information about Ally’s Total Rewards, please visit this link: https://www.ally.com/content/dam/pdf/corporate/ally-total-rewards-snapshot.pdf
Who We Are :
Ally Financial is a customer-centric, leading digital financial services company with passionate customer service and innovative financial solutions. We are relentlessly focused on "Doing it Right" and being a trusted financial-services provider to our consumer, commercial, and corporate customers. For more information, visit www.ally.com .
Ally is an equal opportunity employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to age, race, color, sex, religion, national origin, disability, sexual orientation, gender identity or expression, pregnancy status, marital status, military or veteran status, genetic disposition or any other reason protected by law.
In accordance with the Americans with Disabilities Act, if after review of the position expectations, you believe that you may need a medical accommodation to fulfill the duties of this role, please email [email protected] with details of your accommodation request.
Base Pay Range
$170,000.00 - $300,000.00 An individual's position in the range is determined by the specific role, the scope and responsibilities of the role, work experience, education, certification(s), training, and additional qualifications. We review internal pay, the competitive market, and business environment prior to extending an offer.
Incentive Compensation:
This position is eligible to participate in our annual incentive plan.
Salary Context
This $170K-$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 Ally Financial, 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. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($235K) sits 11% above the category median. Disclosed range: $170K 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.
Ally Financial AI Hiring
Ally Financial has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US. Compensation range: $300K - $300K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.