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About This Role
Job Title:Staff AI Engineer (L4\)
Location:Remote (Must reside within 30 miles of: Portland, ME; Boston, MA; Chicago, IL; Dallas, TX; San Jose, CA; or Seattle, WA)
About the Team
We are a fast\-growing AI Engineering team, driving WEX's strategic vision to integrate artificial intelligence into the core of our product and business. Our team thrives on collaboration, working directly with stakeholders and domain experts to develop innovative AI platforms and solutions that enhance decision\-making and create groundbreaking products that benefit our customers and help them to effectively grow their business. We foster a culture of continuous learning and believe that the best work stems from a shared purpose and an enjoyable, engaging environment.
How You’ll Make an Impact
You are an experienced technical leader and subject matter expert with a deep passion for AI, Machine Learning, and software engineering. You possess a proven track record of architecting scalable systems and solving complex ambiguity using data\-driven tools. You will act as a force multiplier for the team—setting technical standards, mentoring junior engineers, and partnering with product leadership to turn abstract business goals into production\-grade AI solutions.
Responsibilities
- Technical Strategy \& Architecture: Partner with stakeholders to translate complex customer challenges into robust technical designs and AI strategies.
- End\-to\-End Ownership: Architect, build, and deploy high\-performance AI/ML platforms and tools, ensuring scalability, reliability, and security.
- Advanced Model Development: Lead the development, evaluation, fine\-tuning, and monitoring of production\-grade AI/ML models (including LLMs and RAG architectures).
- Operational Excellence: Design and standardize CI/CD pipelines (GitHub Actions) and Infrastructure as Code (Terraform) to ensure reproducible and automated deployments.
- Reliability \& Scale: Proactively monitor system health, lead incident response for critical AI systems, and drive architectural refactoring to remove bottlenecks.
- Mentorship: Mentor L1/L2 engineers, conduct code reviews, and foster a culture of engineering excellence and best practices.
- Innovation: Evaluate emerging AI technologies/vendors and prototype new approaches to keep the organization at the cutting edge of the industry.
Experience You’ll Bring
- 8\+ years of experience as an AI/ML Engineer, Data Scientist, or Software Engineer with a heavy focus on production ML systems.
- Bachelor's degree in Computer Science, Software Engineering, or related field; a Master’s or PhD in AI/ML is preferred.
- Production expertise: Proven track record of taking AI solutions from concept to high\-scale production using Java, C\#, GoLang, or Python.
- LLM Proficiency: Deep experience with Generative AI, including prompting strategies, RAG (Retrieval\-Augmented Generation), fine\-tuning, and agentic workflows.
- System Design: Strong ability to design distributed systems and microservices architectures for model serving.
- Collaboration: Demonstrated ability to lead technical discussions with non\-technical stakeholders and drive consensus.
- Experience in the financial or fintech industry is a plus.
Technical Skills
- Expert proficiency in Python (preferred) or other core languages (Java, Go, C\#).
- Deep knowledge of modern ML frameworks (PyTorch, TensorFlow, Scikit\-learn) and LLM orchestration (LangChain, LlamaIndex, vLLM).
- Advanced understanding of MLOps principles, feature stores, and model monitoring.
- Solid grasp of advanced probability, statistics, and linear algebra.
- Extensive hands\-on experience with Kubernetes (deploying and managing clusters) and Cloud platforms (AWS preferred).
- Strong command of CI/CD methodologies and Infrastructure as Code (Terraform).
- Excellent data engineering skills (SQL, NoSQL, data lakes, and processing pipelines).
*Key Words*
*Staff AI Engineer, MLOps, Generative AI, LLM, RAG, Distributed Systems, Software Architecture, Technical Leadership.*
The base pay range represents the anticipated low and high end of the pay range for this position. Actual pay rates will vary and will be based on various factors, such as your qualifications, skills, competencies, and proficiency for the role. Base pay is one component of WEX's total compensation package. Most sales positions are eligible for commission under the terms of an applicable plan. Non\-sales roles are typically eligible for a quarterly or annual bonus based on their role and applicable plan. WEX's comprehensive and market competitive benefits are designed to support your personal and professional well\-being. Benefits include health, dental and vision insurances, retirement savings plan, paid time off, health savings account, flexible spending accounts, life insurance, disability insurance, tuition reimbursement, and more. For more information, check out the "About Us" section.
Pay Range: $185,000\.00 \- $217,400\.00
Salary Context
This $185K-$217K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At WEX Inc., 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($201K) sits 6% below the category median. Disclosed range: $185K to $217K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
WEX Inc. AI Hiring
WEX Inc. has 2 open AI roles right now. They're hiring across Research Engineer, AI/ML Engineer. Based in Remote, US. Compensation range: $217K - $289K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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