Interested in this AI/ML Engineer role at AZX?
Apply Now →Skills & Technologies
About This Role
About AZX
Our mission is to accelerate positive impact in critical industries through AI transformation.
We’re growing quickly and already work with category\-leaders in real estate (CBRE), energy (LevelTen Energy), logistics (Flexe), and utilities (Puget Sound Energy).
AZX is a public benefit corporation founded in 2024\. We were profitable through bootstrapped consulting for the first year. In early 2026, we raised $6M to scale our operations and technology.
We work on challenges in clean energy, decarbonization, climate risk, energy systems, and global economics. We’re building our company for long\-term success and aim to create the ultimate place to work for those passionate about AI and making a positive impact.
About the Role
You’ll be the engine behind how AZX finds, reaches, and converts prospects into real conversations. This is a new role at a company that believes AI should be at the center of everything we do—including how we go to market.
You’ll build and run the AI\-powered infrastructure that makes our small GTM team punch way above its weight: marketing ops, sales enablement, campaign intelligence, CRM automation, prospect profiling, and AI\-generated content. You’re the person who does two impossible things before noon because nobody told you it was hard.
This role is for someone who lives at the intersection of marketing, data, and AI tooling—and wants to build the GTM stack of the future from scratch.
What You Will Do
- Build and operate AZX’s AI\-powered GTM infrastructure: CRM, marketing automation, campaign tools, analytics, and reporting
- Create AI workflows for prospect intelligence—company profiling, exec move tracking, job posting signals, RFP discovery, news monitoring, and newsletter parsing
- Design and maintain sales enablement systems: meeting transcript pipelines, AI\-generated briefs, proposal drafts, and self\-service content creation tools for the team
- Manage and curate the repository of GTM materials (likely markdown files in GitHub), keeping collateral current and accessible
- Support outreach and campaigns with AI\-generated targeting, personalization, and content
- Build vibe\-coded interactive experiences and content for marketing and thought leadership
- Support community and content efforts with data, automation, and tooling
- Help with event data capture, recruiting support, and internal automation as needed
- Track and report on GTM metrics: pipeline, ABM scorecard movement, funnel conversion, campaign performance
Core Qualifications
- Deeply fluent with AI tools—you use them daily and instinctively, not as an experiment. Claude, ChatGPT, Copilot, or similar are part of how you work.
- Comfortable with developer tools, writing, and understanding code. You can work in GitHub, write scripts, wrangle APIs, handle API keys, and aren’t afraid of a terminal.
- Highly curious and extremely teachable. You learn fast, unlearn faster, and get energy from figuring things out.
- Relentless work ethic. You ship things, finish things, and move fast.
- Systems thinker. You see how tools, data, people, and processes connect and can design workflows that make the whole team more effective.
- Comfortable building from scratch in an ambiguous environment—you don’t need a playbook, you write the playbook.
Values and Culture Qualifications
- Mission aligned—you care about climate, energy, and using technology to solve hard problems. This isn’t just a job.
- Action in ambiguity—you figure out what to do when nobody tells you, and you take ownership of the outcome.
- Intellectual humility—you’d rather find the right answer than be right.
- Kind candor—you care about your teammates and speak honestly, without creating unnecessary conflict.
- Highly productive—you have a track record of getting a lot done.
Other Qualifications (Not required, but a huge plus)
- Experience with CRM platforms (HubSpot, Salesforce, or similar) and marketing automation tools
- Background in B2B marketing, sales ops, or revenue operations
- Familiarity with the energy, utilities, climate, or cleantech space
- Experience at an early\-stage startup or building a function from scratch
- You build side projects, vibe code on weekends, or contribute to open source
Compensation \& Benefits
- Salary range: $70,000–$110,000 (based on capabilities, experience, and location)
- Bonus eligibility
- Equity
- Health insurance with meaningful coverage for dependents
- Flexible paid time off
- Training and learning opportunities
- Fully remote team
- Be part of a fast\-growing, mission\-driven company with industry\-leading clients tackling the massive opportunity of AI transformation in critical industries
Logistics
- Remote, U.S.\-based
- Must be authorized to work in the United States on a full\-time basis
- Willingness to travel for AZX company events and industry conferences
Next Steps — we want to hear from you!
If this job sounds like a fit, we’d love to hear from you. If you feel aligned to the company but don’t check every box, please apply anyway—we’d still love to meet you.
Compensation Range: $70K \- $110K
Salary Context
This $70K-$110K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At AZX, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($90K) sits 46% below the category median. Disclosed range: $70K to $110K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
AZX AI Hiring
AZX has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $110K - $110K.
Location Context
AI roles in Seattle pay a median of $223,600 across 678 tracked positions. That's 22% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.