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About This Role
Director of Applied AI Engineering
North - Remote (US)
Let’s go North, together.
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.
Job Summary:
The Director of Applied AI Engineering will build and lead North's Applied AI team: a hands-on group that takes AI capabilities from prototype to production inside real business workflows. This is a delivery role, not a research role, measured by systems that are live, adopted, and safe to run in a regulated financial services environment. This will require an individual with strong software engineering fundamentals, judgment about where large language models and machine learning outperform conventional automation, attention to detail, and the capacity to rapidly develop and act upon in-depth knowledge of our business, products, customers, and regulatory obligations.
This position will partner with Product, Sales, Marketing, Operations, and Risk to improve current business processes, and will work with Risk, Compliance, Legal, and Information Security to ensure AI systems are explainable, auditable, and safely governed. This role is responsible for providing updates to leadership on the team's impact through clear reporting and KPIs, and for establishing the long-term strategy, technical architecture, and responsible-AI guardrails as well as the short-term scope and sequencing that gets value into production quickly.
What You’ll Be Doing:
- Build and lead a high-performing Applied AI engineering team, defining the practice's operating model, engagement strategy, and technical standards from the ground up.
- Partner with Product, customers, and business stakeholders to observe workflows and turn operational challenges into effective AI solutions.
- Drive rapid discovery, prototyping, and seamless transition of AI capabilities to production across concurrent enterprise projects.
- Architect, deploy, and maintain production AI solutions, including agentic workflows, chatbots, copilots, agent-assist tools, and traditional ML models.
- Establish modular, reusable integrations for AI agents to securely access internal systems and enterprise data with least-privilege controls.
- Own the AI evaluation lifecycle, incorporating offline/online evals, benchmark datasets, human-in-the-loop validation, and production readiness criteria.
- Manage production operations alongside Cloud and Data engineering, overseeing CI/CD, monitoring, incident response, latency, and inference cost optimization.
What We Need From You:
- Bachelor's degree in Computer Science, Engineering, or related field, or equivalent work experience
- 10+ years of experience in software engineering, machine learning, or a related domain, including time in a customer-facing or business-embedded engineering capacity (ex. forward deployed, solutions, or professional services engineering)
- 2+ years building and shipping production systems on top of large language models (LLMs), including at least one agentic or conversational system live with real users
- 4+ years of hands-on management experience with a track record of hiring and growing teams as well as providing coaching and mentoring
- Exceptional leadership skills, and proven ability to operate in a fast-moving environment
- Hands-on approach to closing gaps in technical execution, able to personally debug a misbehaving agent in production
Knowledge, Skills & Abilities:
- Demonstrated problem-solving and executive-level communication skills, bridging technical teams and leadership seamlessly.
- Hands-on expertise in code-level AI SDKs and agent frameworks (e.g., LangGraph, CrewAI, AWS Strands Agents), selecting optimal tools rather than relying on no-code builders.
- Knowledge of core building blocks of production AI applications: tool/function calling, RAG, vector search, structured outputs, conversation and state management, and the judgment to favor deterministic workflows when appropriate.
- Cloud engineering skills (preferably AWS), including deploying agents on managed services (e.g., Bedrock AgentCore), containers, IAM, networking, and cost optimization.
- Track record deploying and maintaining production AI systems, driving robust CI/CD, tracing, incident response, and latency/cost controls.
- Experience making evaluation part of the build cycle, including test sets, regression testing, human review, and measuring quality once systems are live
- Grasp of AI security and guardrails (prompt injection defense, least-privilege access, audit trails, and human-in-the-loop design) aligned with financial regulations (Model Risk Management, SOC 2, PCI DSS, GLBA).
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
Salary Range: $207,000-290,000 USD
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.
Please note: North is a US-based company and no sponsorship is available for this position at this time.
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 $207K-$290K 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 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 Required
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 ($248K) sits 17% above the category median. Disclosed range: $207K to $290K.
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.
North. AI Hiring
North. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $290K - $290K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 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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