Interested in this AI/ML Engineer role at North.?
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Corporate Counsel
(Product, Privacy, Cybersecurity, AI and Technology Compliance)
North \- Remote
(Preference given to candidates located in the Eastern and Central time zones)
Join our mission to build the largest suite of credit card processing and merchant services. It’s one simple payment platform backed by the most diverse payment companies. From credit card processing to back\-office management, North points the way to smarter, faster, and just plain better payment solutions.
*Let’s go North, together.*
Under direction of the General Counsel and the Associate General Counsel, VP, the Corporate Counsel position is primarily responsible for assisting with a variety of in\-house legal responsibilities including supporting projects related to North’s product, data privacy, AI and cybersecurity ecosystem.
The Corporate Counsel will support our Legal team in advising the company and its executive management and their teams on product, data, data privacy, cybersecurity and AI related legal issues. This role will assist with identifying, developing and implementing processes and programs designed to enhance efficiencies concerning the privacy function at North.
What you'll do:
- Provide hands\-on, business oriented and practical legal counsel including business process mapping and enhancements.
- Partner with all company divisions and functions to meet objectives related to these areas of responsibility.
- Support General Counsel and Associate General Counsel, (AGC), VP in matters related to product development as well as developing and/or deploying AI and related technological innovations.
- Support General Counsel and AGC, VP in matters related to data privacy and cybersecurity.
- Partner with company divisions and functions to provide business and practical advice so that objectives can be met.
- Provide strategic legal guidance on development and deployment of AI, privacy regulations, data governance and responsible data use, including evolving legal frameworks.
- Analyze emerging legal, regulatory and policy frameworks related to AI, data privacy and cybersecurity, proactively developing compliance strategies alongside business stakeholders.
- Partner with compliance and business teams to develop scalable approaches to regulatory compliance and risk management.
- Ensure the company’s policies and strategies are in compliance with industry and governmental regulations.
What we need from you:
- Juris Doctorate from an accredited law school.
- 5\+ years of combined, relevant experience as an in\-house counsel and/or private practice attorney with an expertise in data privacy, cybersecurity, product development, marketing and/or AI law.
- Experience working in a fast\-moving technology company.
- General familiarity with consumer financial services laws, as well as the FTC.
- Payment or financial services industry experience preferred.
- Excellent presentation, verbal and written communication skills.
- Strong contract review, negotiation, analytical, and creative skills.
- Strong ability to multi\-task, prioritize and complete numerous projects simultaneously.
- Ability to collaborate and effectively interface with employees at all levels of the organization.
- Proficient with Google software applications.
- Ability to think strategically and compare alternative courses of action to make sound decisions and give business and legal advice in a compressed time frame often with limited information.
Travel Requirements: 5 \- 10%
Salary Range: $110,000\- $180,000
Pay within this range varies by work location and on job\-related knowledge, skills, and experience. We look forward to discussing your salary expectations and our full total rewards offerings throughout the interview process.
What we offer:
We offer a comprehensive benefits package that enables our teams to live a *life well lived,* both personally and professionally. Some of our perks include:
- Medical, Dental, \& Vision Coverage
- Flexible Paid Time Off
- 401(k) \+ Match
- Mental Health Support \& Well\-Being Program
- Paid Maternity \& Paternity Leave
- Education Assistance
- Company\-funded Lifestyle Spending Account
Who we are:
North, and our family of companies, are committed to helping entrepreneurs grow their businesses. As an end\-to\-end payment solutions company, we provide everything business owners need to get paid, whether they serve customers in a physical storefront, online, or both. We pride ourselves on being large enough to offer customized solutions to our enterprise\-level clients while remaining agile enough to take an award\-winning, hands\-on approach to personal service that our merchants won’t find anywhere else.
Let’s go North, together! Our most important resource is our people. Join our diverse team of innovators and do\-ers and make your mark on the future of payments technology. We're proud to offer benefits that help our team members further their overall well\-being through unique initiatives that are both personally and professionally fulfilling.
At North, we celebrate diversity and create an inclusive environment for everyone. We are an equal opportunity employer.
*To learn more about North, and our family of companies, visit our website:* north.com
Salary Context
This $110K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At North., 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($145K) sits 34% below the category median. Disclosed range: $110K to $180K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
North. AI Hiring
North. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $180K - $180K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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