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:
Top technical subject matter expert providing technical and risk management expertise in improving and expanding the observability and reporting on the Security Posture of the Company. This position supports the enterprise\-wide Information Security program as a trusted technical advisor by empowering team members to make risk\-aware decisions in accordance with Early Warning’s compliance, regulatory and departmental policy and procedures. Provides the primary measure of confidence that the security features, practices, procedures, and architecture of the security program accurately mediate and enforce the security policy.
Essential Functions:
- Create and manage the operating model for identifying and resolving security risks and posture drift.
- Lead initiatives and strategies in support of the Security Posture Management program.
- Own ensuring that business goals and risks are adequately addressed; collaborate and consult with enterprise cross\-functional teams to maintain continuous awareness of the enterprise’s Security Posture and identify improvement opportunities.
- Establish effective working relationships across the enterprise to foster a strong risk culture by supporting stakeholders in owning and managing their risks and controls.
- Ensure controls are successfully designed and implemented to meet Compliance requirements and business objectives. Manage relationships cross\-functionally to integrate alignment with organizational strategies while addressing risk management needs.
- Review new processes from a control's perspective. Consult with process owners to design and implement new controls.
- Improve risk and control environment by providing subject matter technical expertise on enhancing the design and effectiveness of Security’s control program while aligning with compliance and technical needs.
- Develop, execute, and present the risk reporting framework ensure risk mitigation activities are performed timely. Select appropriate metrics (KRI/KPI’s) to monitor adequacy and effectiveness of the control environment. Monitor remediation efforts to closure, including review of supporting evidence.
- Develop and enhance documentation and reporting standards to support oversight of the enterprise’s Security Posture and ensure consistent execution and coverage across the enterprise supported through a stakeholder\-approved policy\-driven governance program.
- Supports the company's commitment to risk management and protecting the integrity and confidentiality of systems and data.
Minimum Qualifications:
- Education and experience typically obtained through completion of a bachelor’s degree in computer science, Information Technology, Cyber Security, or related field.
- Typically 15 or more years of progressive related information technology or information security work experience with various types of information security\-related technologies. including firewalls, IDS, vulnerability management, anti\-virus, data loss prevention, two factor authentication, and VPN.
- 3 or more years of experience with the security, regulatory, and privacy controls environment, and security governance, regulatory landscape, risk assessment, and risk management principles and techniques.
- Demonstrated advanced level experience with network security design and protection, application development, application security issues, operating system security, hardening standards and protection mechanisms.
- Strong technical knowledge in the area of security tools, application security design and architecture; secure network design and architecture, server security, and workstation security.
- Ability to articulate the practical and technical application of the following standards: (ISO, PCI and NIST/FISMA)
- Strong understanding and experience with Information technology systems and processes, network infrastructure, data architecture, data processes, protocols, and auditing and monitoring processes.
- Strong understanding and experience with Cyber and cloud security standard frameworks, architecture, design, operations, controls, technology, solutions, and service orchestration.
- Experience evaluating process and configurations for compliance with policies and regulations.
- Respected subject matter expert with high level of integrity, effective interpersonal and communication skills, and executive presence.
- Strong experience developing and tracking information security related KPIs and KRIs.
- Background and drug screen.
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.
Preferred Qualifications:
- Expertise in ISO 27002, PCI DSS 3\.2 or current, NIST 800\-53a, SIG, FFIEC handbooks, SOC2 Type II, GLBA, FCRA, NYDFS, data privacy.
- Certification in one of CISA, CISM, CISSP, CCSP, CRISC, GSNA, CGIH, or equivalent
- Project or Process management experience.
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: $221,000 \- $276,000\.
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!
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
*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.*
Salary Context
This $221K-$276K range is above the 75th percentile 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($248K) sits 34% above the category median. Disclosed range: $221K to $276K.
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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