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About This Role
#### Join Axon and be a Force for Good.
At Axon, we're on a mission to Protect Life. We're explorers, pursuing society's most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and each other.
Life at Axon is fast\-paced, challenging and meaningful. Here, you'll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter.
AI/Technology Evangelist \- Program Manager (Corporate AI Team)
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Team \& Role Overview
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Axon's Corporate AI Team sits within the Enterprise Data organization and focuses on internal\-facing AI solutions that help Axon employees do more high‑value work with less manual effort. The team builds and operates Cortex, Axon's internal AI platform that provides GPT‑class models, chat assistants, and secure integrations with systems like Quip, Jira, Slack, Microsoft 365, Snowflake, and more.
We're looking for an AI/Technology Evangelist to drive the adoption, understanding, and responsible use of AI across Axon. In this role, you'll:
- Be the bridge between the Corporate AI Team's capabilities and the rest of the organization—translating what's possible into what's practical.
- Design and deliver training programs, workshops, demos, and enablement materials that help employees at every level use AI tools effectively and safely.
- Identify high\-impact AI use cases across business units, champion them from concept to adoption, and measure their impact.
- Build and maintain the internal AI playbook—guides, best practices, templates, and reference materials that make it easy for teams to build with AI.
This is a hands\-on senior IC role. You'll spend most of your time working directly with teams across the company, creating content, running programs, and evangelizing AI capabilities—not managing people.
What You'll Do
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### Adoption \& Enablement
- Design, build, and deliver training programs (workshops, office hours, lunch\-and\-learns, self\-paced content) that help Axon employees effectively use AI tools including Cortex, Claude, Copilot, and internal AI\-powered applications.
- Create and maintain a library of enablement materials—quick\-start guides, prompt engineering playbooks, video walkthroughs, FAQs, and best\-practice documentation.
- Run an internal AI champions program (e.g., Champions Circle) to cultivate power users across business units who can drive adoption within their teams.
- Track and report on adoption metrics—usage, engagement, satisfaction, and business impact—to measure progress and identify gaps.
### Use Case Discovery \& Delivery
- Partner with teams across Axon (Finance, Sales, Operations, Legal, HR, Support, Engineering) to identify workflows where AI can meaningfully reduce toil, improve quality, or create leverage.
- Prioritize use cases based on impact, feasibility, and alignment with Axon's mission; work with the Corporate AI Team to scope and deliver solutions.
- Build lightweight prototypes and demos that show teams what's possible before committing engineering resources.
- Document and share success stories—quantified wins, before/after workflows, and lessons learned—to build momentum across the organization.
### Communication \& Storytelling
- Serve as the primary internal voice for Axon's AI platform—communicating roadmap updates, new capabilities, best practices, and success stories to all levels of the organization.
- Create compelling content (blog posts, internal newsletters, Slack updates, demo videos, presentations) that keeps AI visible and accessible.
- Present to leadership and executive audiences on AI adoption progress, emerging opportunities, and strategic recommendations.
- Represent Axon's Corporate AI work externally where appropriate—conferences, meetups, industry events, and thought leadership content.
### Security, Governance \& Responsible AI
- Educate teams on safe and responsible use of AI tools—covering topics like secrets management, data classification, prompt injection risks, and compliance requirements (CJIS, FedRAMP, SOC 2\).
- Develop and maintain a "Vibe Coding" safety guide and similar resources that help employees understand the risks of AI\-generated code and content.
- Partner with Security, Legal, and Governance teams to ensure AI adoption programs reflect Axon's policies and ethical commitments.
- Help operationalize responsible AI guardrails—model selection guidance, data minimization, usage controls, and audit logging.
### Ecosystem \& Community Building
- Build and maintain a clear map of AI initiatives happening across Axon—connecting teams solving similar problems and preventing duplication of effort.
- Stay current with the rapidly evolving AI landscape (new models, tools, techniques, risks) and translate what matters into actionable guidance for the organization.
- Evaluate new AI tools and vendors; make recommendations about what to adopt, pilot, or avoid.
- Contribute to Axon's external AI community presence through talks, blog posts, open\-source contributions, or partnerships.
What You Bring
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- Bachelor's degree in Computer Science, Engineering, Communications, or a related field, or equivalent practical experience.
- 8\+ years of experience in technology roles, with at least 3 years focused on AI/ML, developer relations, technical evangelism, enablement, or a closely related domain.
- Demonstrated ability to explain complex technical concepts to non\-technical audiences—in writing, in presentations, and in one\-on\-one conversation. This is the core skill.
- Hands\-on experience with modern AI tools and platforms (LLMs, coding assistants, automation frameworks, cloud AI services). You don't need to be a researcher, but you need to be a skilled practitioner.
- Strong understanding of software development practices, cloud infrastructure, and enterprise technology stacks—enough to have credible technical conversations with engineers.
- Track record of driving technology adoption or transformation initiatives within an organization. You've changed how people work, not just told them to.
- Self\-directed and comfortable operating with ambiguity. You don't wait for a roadmap—you build one.
- Strong communication and collaboration skills, with comfort working across every level of an organization, from individual contributors to C\-suite executives.
Preferred Experience
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You don't need all of these, but experience in several of the following will help you ramp quickly:
- Experience in developer relations, developer advocacy, technical evangelism, or technical training at a technology company.
- Hands\-on work with LLM platforms and tools, such as OpenAI (ChatGPT / APIs), Anthropic (Claude / Claude Code), AWS Bedrock, Azure OpenAI, or similar.
- Experience building and running internal enablement programs—training curricula, champions programs, office hours, documentation libraries.
- Familiarity with AI governance, responsible AI frameworks, and compliance requirements (CJIS, FedRAMP, SOC 2\).
- Experience integrating with enterprise systems like Jira, Confluence/Quip, Slack, M365, Salesforce, or Snowflake—enough to understand what's possible and demo it.
- Public speaking experience—conferences, webinars, all\-hands presentations, customer events.
- Background in public safety, government technology, or regulated industries.
- Understanding of prompt engineering, RAG architectures, agentic workflows, and AI evaluation methods.
- Content creation skills—writing, video production, or visual design experience that helps you create compelling enablement materials.
Don't meet every single requirement? That's ok. At Axon, we Aim Far. We think big with a long\-term view because we want to reinvent the world to be a safer, better place. We are also committed to building diverse teams that reflect the communities we serve.
Studies have shown that women and people of color are less likely to apply to jobs unless they check every box in the job description. If you're excited about this role and our mission to Protect Life but your experience doesn't align perfectly with every qualification listed here, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
Important Notes
*The above job description is not intended as, nor should it be construed as, exhaustive of all duties, responsibilities, skills, efforts, or working conditions associated with this job. The job description may change or be supplemented at any time in accordance with business needs and conditions.*
*Some roles may also require legal eligibility to work in a firearms environment.*
*We collect personal information from applicants to evaluate candidates for employment. You may request access, deletion, or exercise other CCPA rights at* *[email protected]* *or via our* *Axon Privacy Web Form**. For more information, please see the Your California Privacy Rights section of our* *Applicant and Candidate Privacy Notice.*
*Axon's mission is to Protect Life and is committed to the well\-being and safety of its employees as well as Axon's impact on the environment. All Axon employees must be aware of and committed to the appropriate environmental, health, and safety regulations, policies, and procedures. Axon employees are empowered to report safety concerns as they arise and activities potentially impacting the environment.*
*We are an equal opportunity employer that promotes justice, advances equity, values diversity and fosters inclusion. We're committed to hiring the best talent — regardless of race, creed, color, ancestry, religion, sex (including pregnancy), national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, genetic information, veteran status, or any other characteristic protected by applicable laws, regulations and ordinances — and empowering all of our employees so they can do their best work. If you have a disability or special need that requires assistance or accommodation during the application or the recruiting process, please email* *[email protected].* *Please note that this email address is for accommodation purposes only. Axon will not respond to inquiries for other purposes.*
Phishing alert: Axon will never ask you to pay for any part of the hiring process, including training, equipment, or background checks. We do not make job offers via text message, WhatsApp, or instant messaging platforms without a formal interview process. All legitimate job openings are listed on our official careers page at https://www.axon.com/careers. If you receive a suspicious offer or outreach from an email address that is not @axon.com, or if you are asked for sensitive personal information (bank details, Social Security Number) prematurely, please ignore the message and report it to [email protected].
Salary Context
This $147K-$236K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Axon, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($191K) sits 6% above the category median. Disclosed range: $147K to $236K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Axon AI Hiring
Axon has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Boston, MA, US. Compensation range: $236K - $271K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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