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About This Role
GA-Intelligence is seeking an Arctic Mission Analyst / Forward Deployed AI Engineer to support mission partners at NSA Alaska and the 611th Air Operations Center. This is a hybrid tradecraft-and-engineering role, and it is a forward deployed one: you will sit with the customer full time, not visit them. The expectation is to understand the distinct mission tradecraft at both locations – cryptologic production and support to national intelligence stakeholders at NSAA, and air battle management, ATO/ACO execution, and air sovereignty operations in support of Alaskan NORAD Region/ALCOM at the 611AOC, concurrently building those workflows. You will apply AI and machine learning tooling at the edge, prototyping in the operator's environment, on the operator's networks, against the operator's real problems. Working under the direction of the regional Mission Lead, you will be GA-Intelligence's on-site presence in Alaska: the person analysts walk over to when something isn't working, and the person who ships and trains the fix.
DUTIES AND RESPONSIBILITIES:
- Embed full time at NSA Alaska and the 611 AOC as GA-Intelligence's resident mission expert and forward deployed engineer, building trusted working relationships with government counterparts at both organizations
- Develop, deploy, and sustain AI-enabled analytic capability directly in the customer environment, working inside accreditation and network constraints rather than around them
- Apply large language models, machine learning, and automation to compress analyst workflows — tipping and cueing, correlation, triage, reporting, and product generation
- Learn and maintain fluency in the tradecraft at each location, including SIGINT production and dissemination practices at NSAA and air operations center battle rhythm, mission planning, and execution processes at the 611 AOC
- Maintain deep awareness of the Arctic and high-latitude operating environment, including homeland defense and air sovereignty missions, approaches to the ADIZ, maritime activity in the Bering and Arctic, and evolving threats and command relationships across NORAD & USNORTHCOM and subordinate organizations
- Identify opportunities to innovate on intelligence and operations tradecraft, introducing new methods, tools, and analytic approaches that measurably improve mission outcomes
- Translate operator and analyst feedback into clear, actionable requirements for engineering and research teams, and close the loop when capability ships
- Support test, evaluation, and refinement of capabilities against real-world mission use cases, exercises, and real-world events
- Drive adoption — train users, build trust in model outputs, and be honest with operators about what the tooling can and cannot do
- Represent GA-Intelligence's mission and technical capabilities to government stakeholders in theater, and surface expansion opportunities back to the company
We recognize and appreciate the value and contributions of individuals with diverse backgrounds and experiences and welcome all qualified individuals to apply.
Job Qualifications
- Minimum of 10 years of mission experience supporting SIGINT operations, air operations, or Homeland/Arctic defense, with direct experience at an NSA cryptologic center, an Air Operations Center, or a directly comparable operational environment
- Demonstrated working knowledge of the tradecraft at NSA Alaska and/or the 611 AOC, or a credible path to that fluency within the first 90 days
- Hands-on technical ability — proficiency in Python, comfort with APIs, data pipelines, and notebooks, and practical experience applying AI/ML or LLM-based tooling to operational problems
- Demonstrated track record of delivering capability into an operational environment and getting it used
- Comfort operating independently at a remote site with limited on-site engineering support, and sound judgment about when to escalate
- Exceptional communication skills, including written, verbal, and presentation abilities
- Bachelor's Degree
- Willingness to relocate to and be based in Anchorage, AK full time, with travel to remote Alaska sites and CONUS as needed
PREFERRED SKILLS:
- Prior experience as a forward deployed engineer or embedded technical lead with a government customer
- Familiarity with NORAD & USNORTHCOM mission sets, air defense operations, or Arctic domain awareness programs
- Experience deploying software or models within classified accreditation boundaries
- Background in data fusion, multi-INT correlation, or track-level analytics
Job Category
Product Support
Experience Level
Senior (8+ years)
Workstyle
Remote
Full-Time/Part-Time
Full-Time Salary
Pay Range Low
99,500
Pay Range High
170,750
Travel Percentage Required
0% - 25%
Relocation Assistance Provided?
Yes
US Citizenship Required?
Yes
Clearance Required?
Yes
Clearance Level
Top Secret (current SCI access with CI Poly)
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Salary Context
This $99K-$170K range is in the lower quartile 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 General Atomics, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($135K) sits 36% below the category median. Disclosed range: $99K to $170K.
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.
General Atomics AI Hiring
General Atomics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Wainwright, AK, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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