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About This Role
About the Company
Armada is the hyperscaler for the edge, delivering modular AI infrastructure from first deployment to AI factory with speed, scale and sovereignty. Named one of Fast Company's Most Innovative Companies and to the CNBC Disruptor 50, Armada's solutions are deployed in over 60 countries globally for organizations ranging from energy to defense.
With nearly $500 million in funding to date, Armada is backed by leading investors including Founders Fund, Lux, BlackRock and Microsoft (M12\), alongside strategic partnerships with Microsoft, Dell, Palantir, NVIDIA, SpaceX, and Skydio. We are building the infrastructure layer for sovereign and edge AI \- rugged, deployable compute for customers that cannot rely on centralized cloud.
Working at Armada means taking ownership, driving autonomy, and delivering impact. You'll tackle challenges that haven't been solved before and help build something transformative from the ground up. What you do here will not only define your career but help further Armada's mission to bridge the digital divide for customers around the world.
About the Role
We are seeking a patient, articulate, and technically fluent Part\-Time Technical Trainer to join our team on an on\-demand basis. This role sits closer to technical marketing than software development — you will need to understand and confidently use our GPUaaS platform and surrounding ecosystem, but you are not expected to develop on top of it. Your primary focus will be two\-fold: (1\) building customer\-centric demo content that replaces engineering\-centric recordings with relatable, use\-case\-driven flows, and (2\) delivering hands\-on, guided training sessions at customer sites around the world. Work is project\-based, averaging roughly one week per month, and is ideal for an experienced professional who wants to stay engaged without a full\-time commitment.
This is a non\-converting contract engagement. Hours and travel are not guaranteed week\-to\-week; when a training trip is scheduled, you go — when it is not, there is no work. The predictable portion of the role is demo and curriculum creation; the training delivery side fluctuates. If you are a seasoned technical trainer or solutions engineer looking for meaningful part\-time work, this role is built for you.
Location: This role is remote within the continental United States.
What You'll Do (Key Responsibilities)
Customer Training \& Enablement
- Design, develop, and deliver hands\-on technical training for enterprise customers — not slide presentations, but guided, interactive sessions where customers execute steps in their own environments with you by their side.
- Travel globally to customer sites (domestic and international) to conduct in\-person training; deliver remote sessions when travel is not required.
- Tailor curriculum and pacing to your audience — primarily hands\-on technical practitioners and their managers (senior manager / director level); this is not a C\-suite audience.
- Conduct needs\-assessments before each engagement to align training content with customer use cases and success metrics.
- Provide post\-training follow\-up support, Q\&A sessions, and supplementary materials to reinforce learning.
Content Creation \& Demo Production
- Own the transition from engineering\-centric demos to customer\-centric ones: learn Armada's identified product flows, set up the environment, record polished walkthroughs with clear voiceover, and publish content that customers can relate to. Assume net\-new creation — demos go stale approximately every three months as new features ship.
- Create and maintain a library of enablement artifacts: quick\-start guides, how\-to articles, sample code notebooks, architecture diagrams, and slide decks.
- Build step\-by\-step lab flows that customers can follow independently; the same flows should double as training material, creating a single content library that serves both purposes.
- Collaborate with Product and Engineering to translate new feature releases into clear, customer\-ready training content.
- Leverage AI productivity tools (e.g., AI video editors, scripting assistants, image generation, and documentation tools) to accelerate content production.
Technical Demonstrations \& Evangelism
- Deliver live technical demos at customer discovery calls, webinars, conferences, and partner events.
- Act as a credible technical voice, demonstrating GPU workload provisioning, cluster management, performance tuning, and cost optimization on our platform.
- Gather feedback during training engagements and relay actionable insights to Product and Customer Success teams.
Continuous Improvement
- Keep curriculum current with evolving GPUaaS product features, industry frameworks (PyTorch, CUDA, Kubernetes, etc.), and emerging AI/ML trends.
- Track training effectiveness through assessments, surveys, and usage analytics; iterate content based on results.
- Contribute to a knowledge base and internal trainer certification program as the team scales.
Required Qualifications
Technical Expertise
- 10–15 years of overall professional experience in technical roles, with at least 3 years focused on cloud infrastructure, GPU computing, or HPC environments.
- Proficiency with Linux, containers (Docker/Kubernetes), and cloud CLI tooling.
- Working knowledge of at least one deep\-learning framework (PyTorch, TensorFlow, JAX) and GPU programming fundamentals (CUDA, cuDNN, or similar).
- Comfortable reading and writing Python; ability to build clear, reproducible Jupyter notebooks for instructional use.
Training \& Communication
- Demonstrated ability to explain complex technical concepts clearly and concisely to both technical and non\-technical audiences — articulateness is non\-negotiable.
- 3\+ years of formal training, instructional design, or technical enablement experience; curriculum development experience required — not just delivery.
- Exceptional verbal and written English communication skills; comfortable presenting to groups of all sizes.
- Patient, methodical teaching style — able to slow down, take a breath, and guide customers through technical content step by step without frustration.
Content Production
- Experience producing instructional or demo videos (screen recording, voiceover, light editing) using tools such as Camtasia, Loom, DaVinci Resolve, or equivalent.
- Proficiency with AI productivity tools — e.g., AI writing assistants, automated transcription/captioning, AI image/diagram generators, and prompt\-based video editors.
- Strong documentation skills; ability to produce polished slide decks, technical guides, and quick\-reference cards.
Logistics
- Must hold a valid passport; travel is global and may include international customer sites.
- Comfortable with an on\-demand travel schedule — when a training engagement is scheduled, you are expected to travel; there is no guaranteed frequency or fixed number of trips per month.
- Reliable home\-office setup with high\-speed internet for remote training delivery.
Preferred Qualifications:
- Prior experience in a cloud/HPC vendor, GPU OEM, or AI infrastructure company.
- Familiarity with MLOps practices (MLflow, W\&B, Kubeflow) and distributed training paradigms.
- Certifications in relevant platforms: AWS, GCP, Azure, NVIDIA DLI, Kubernetes (CKA/CKAD), or similar.
- Experience with LMS platforms (Docebo, Teachable, Moodle) for publishing and tracking online courses.
- Background in developer relations, technical sales engineering, or solutions architecture.
What We Offer:
- Truly flexible, on\-demand engagement — ideal for an experienced professional seeking meaningful part\-time work without a full\-time commitment. Hours are not guaranteed week\-to\-week; this role will not convert to full\-time.
- $110–$140/hr contract rate.
- Full travel and expense reimbursement for all customer\-site visits.
- Access to our full GPUaaS platform for self\-directed learning, demo prep, and content creation.
- Collaborative team culture with direct access to Product and Engineering leadership.
- Opportunity to grow with a fast\-moving company at the forefront of AI infrastructure.
Compensation
For U.S. Based candidates: To ensure fairness and transparency, the starting base salary range for this role for candidates in the U.S. are listed below, varying based on location experience, skills, and qualifications.
\#LI\-ST1
\#LI\-Remote
You're a Great Fit if You're
- A go\-getter with a growth mindset. You're intellectually curious, have strong business acumen, and actively seek opportunities to build relevant skills and knowledge
- A detail\-oriented problem\-solver. You can independently gather information, solve problems efficiently, and deliver results with a "get\-it\-done" attitude
- Thrive in a fast\-paced environment. You're energized by an entrepreneurial spirit, capable of working quickly, and excited to contribute to a growing company
- A collaborative team player. You focus on business success and are motivated by team accomplishment vs personal agenda
- Highly organized and results\-driven. Strong prioritization skills and a dedicated work ethic are essential for you
Equal Opportunity Statement
At Armada, we are committed to fostering a work environment where everyone is given equal opportunities to thrive. As an equal opportunity employer, we strictly prohibit discrimination or harassment based on race, color, gender, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other characteristic protected by law. This policy applies to all employment decisions, including hiring, promotions, and compensation. Our hiring is guided by qualifications, merit, and the business needs at the time.
Unsolicited Resumes and Candidates
Armada does not accept unsolicited resumes or candidate submissions from external agencies or recruiters. All candidates must apply directly through our careers page. Any resumes submitted by agencies without a prior signed agreement will be considered unsolicited and Armada will not be obligated to pay any fees.
Salary Context
This $228K-$291K range is above the 75th percentile 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 ARMADA, 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 $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 ($260K) sits 19% above the category median. Disclosed range: $228K to $291K.
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.
ARMADA AI Hiring
ARMADA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bellevue, WA, US. Compensation range: $291K - $291K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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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