Interested in this AI/ML Engineer role at Early Warning Services?
Apply Now →About This Role
At Early Warning, we’ve powered and protected the U.S. financial system for over thirty years with cutting\-edge solutions like Zelle®, Paze℠, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses.
Positions located in Scottsdale, San Francisco, Chicago, or New York follow a hybrid work model to allow for a more collaborative working environment.
Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship.
Overall Purpose
The Director of Enterprise Risk Management (ERM) is a critical member of the Enterprise and Operational Risk Management team. As owner of the Company’s Enterprise Risk Management Framework and Policy, the Director ERM is responsible for driving the core components and maturity of Early Warning’s ERM Program, as well as contributing to the management of risks that may impede the achievement of enterprise strategic goals and objectives. A key component of the role will be to work with all business areas across the company, as well as with other second and third\-line functions, to support the evolution and delivery of the ERM program and drive a strong risk culture across the organization.
Essential Functions
- Lead the management and maturity of the ERM Program, including overall risk governance, policies/standards/procedures, risk identification and assessment methodology, risk taxonomy, risk appetite development, risk education and culture and coordination of materials various executive and board level risk committees
- Develop and maintain employee education activities designed to promote a strong culture of risk management and provide training on core elements of the risk management framework, including the three lines of defense
- Conduct enterprise and other risk assessments, as needed to identify and assess significant risks, including working with the analytics team to model risks and understand all impact, worst case scenarios, etc.
- Lead the periodic refresh of the Company’s Risk Appetite Statement, including owning and maintaining procedures to manage the Company\-wide risk appetite process, driving processes to link risk appetite to strategic planning and engaging business partners on adoption and adherence
- Provide oversight, review/assessment, and guidance of the first\-line’s risk management programs and report to senior leadership on enterprise risks and program status
- Lead and support the implementation and maturity of a new Integrated Risk Management Platform and drive user adoption across the enterprise
- Lead the ongoing evolution of the enterprise policy governance program and processes
- Ensure close coordination between ERM and the annual strategic planning process, budgeting and project prioritization
- Working closely with Operational Risk Management and other 1st and 2nd line teams, identify, report, and monitor emerging risks to the company
- Contribute to the reporting of enterprise\-level risks to the company’s functional business leaders, executives, and governance committees
- Lead a team of Risk Managers in the performance of efficient, effective, and sound risk management activities to connect objectives, risk appetite, and execution in an integrated manner that helps the Company create, preserve, and realize value.
- Provides effective leadership in developing highly engaged, high\-performance teams.
- Support the company’s commitment to protect the integrity and confidentiality of systems and data.
Minimum Qualifications
- Typically, a minimum of 12 years or more of progressive experience in risk management, preferably in a regulated industry
- Proven in\-depth knowledge of risk management programs, best practices, methodologies and frameworks typically gained through related experience
- Demonstrated experience within a three\-lines of defense program
- Understanding of risk management and internal control leading practices
- General knowledge of regulatory requirements specific to the financial services industry
- Excellent oral and written communication skills and ability to influence and guide others at all levels of the organization
- Proven strategic thinker and creative problem solver who also demonstrates strong attention to detail and efficiency
- Ability to navigate ambiguous situations and drive decision\-making
- Ability to manage change and learn quickly in a dynamic business environment.
- Strong relationship building skills
- Excellent organizational, analytical and project management skills
- Proficient in Microsoft Excel and PowerPoint.
- Background and drug screen.
Preferred Qualifications
- Advanced degree in a relevant field (e.g., JD, MBA, PhD)
- Knowledge of the payments and/or financial services industries, regulatory trends and competitive environment
- Knowledge and understanding of COSO’s Enterprise Risk Management framework
- Experience with Integrated Risk Management Platforms
The above job description is not intended to be an all\-inclusive list of duties and standards of the position. Incumbents will follow instructions and perform other related duties as assigned by their supervisor.
Physical Requirements
Working conditions consist of a normal office environment. Work is primarily sedentary and requires extensive use of a computer and involves sitting for periods of approximately four hours. Work may require occasional standing, walking, kneeling and reaching. Must be able to lift 10 pounds occasionally and/or negligible amount of force frequently. Requires visual acuity and dexterity to view, prepare, and manipulate documents and office equipment including personal computers. Requires the ability to communicate with internal and/or external customers.
Employee must be able to perform essential functions and physical requirements of position with or without reasonable accommodation.
The base pay scale for this position in:
New York, NY/ San Francisco, CA in USD per hour is: $186,000 \- $232,000\.
Additionally, candidates are eligible for a discretionary incentive plan and benefits.
This pay scale is subject to change and is not necessarily reflective of actual compensation that may be earned, nor a promise of any specific pay for any specific candidate, which is always dependent on legitimate factors considered at the time of job offer. Early Warning Services takes into consideration a variety of factors when determining a competitive salary offer, including, but not limited to, the job scope, market rates and geographic location of a position, candidate’s education, experience, training, and specialized skills or certification(s) in relation to the job requirements and compared with internal equity (peers). The business actively supports and reviews wage equity to ensure that pay decisions are not based on gender, race, national origin, or any other protected classes.
Some of the Ways We Prioritize Your Health and Happiness
- Healthcare Coverage – Competitive medical (PPO/HDHP), dental, and vision plans as well as company contributions to your Health Savings Account (HSA) or pre\-tax savings through flexible spending accounts (FSA) for commuting, health \& dependent care expenses.
- 401(k) Retirement Plan – Featuring a 100% Company Safe Harbor Match on your first 6% deferral immediately upon eligibility.
- Paid Time Off – Flexible Time Off for Exempt (salaried) employees, as well as generous PTO for Non\-Exempt (hourly) employees, plus 11 paid company holidays and a paid volunteer day.
- 12 weeks of Paid Parental Leave
- Maven Family Planning – provides support through your Parenting journey including egg freezing, fertility, adoption, surrogacy, pregnancy, postpartum, early pediatrics, and returning to work.
And SO much more! We continue to enhance our program, so be sure to check our Benefits page here for the latest. Our team can share more during the interview process!
*Early Warning Services, LLC (“Early Warning”) considers for employment, hires, retains and promotes qualified candidates on the basis of ability, potential, and valid qualifications without regard to race, religious creed, religion, color, sex, sexual orientation, genetic information, gender, gender identity, gender expression, age, national origin, ancestry, citizenship, protected veteran or disability status or any factor prohibited by law, and as such affirms in policy and practice to support and promote equal employment opportunity and affirmative action, in accordance with all applicable federal, state, and municipal laws. The company also prohibits discrimination on other bases such as medical condition, marital status or any other factor that is irrelevant to the performance of our employees.*
*Early Warning Services LLC is a proud participant in E\-Verify, a federal program to help ensure a legal and authorized workforce. As part of our hiring process, we electronically verify the employment eligibility of all new hires through E\-Verify. For more information on your rights and responsibilities under E\-Verify please visit* *Home \| E\-Verify**.*
Salary Context
This $186K-$232K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Early Warning Services, 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($209K) sits 13% above the category median. Disclosed range: $186K to $232K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Early Warning Services AI Hiring
Early Warning Services has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $193K - $276K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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