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About This Role
Work Arrangement: Hybrid
Clearance: Active Secret w/TS Capability
Foxhole Technology provides robust cybersecurity and IT support capabilities for federal civilian and defense agencies. A recognized leader in navigating technology and security challenges, Foxhole delivers mission\-focused innovations to answer evolving and complex needs. Our talented employee\-owners provide agile, scalable services and solutions that solve operational gaps, operate critical systems, and protect and secure the enterprise – across the organization and around the world.
Foxhole Technology is seeking an AI Evaluation SME to join an existing program. The AI Evaluation SME will support the assessment, testing, validation, and operational evaluation of artificial intelligence, machine learning, automation, analytics, and decision\-support capabilities being considered for or integrated into the Navy's Next Generation CANES environment. This role will help ensure AI\-enabled capabilities are mission\-relevant, reliable, secure, explainable, measurable, and suitable for deployment within afloat, tactical, disconnected, intermittent, limited\-bandwidth, and multi\-security\-domain environments.
The SME will develop evaluation frameworks, test methods, metrics, datasets, scenarios, risk assessments, and reporting products that help Navy and CACI stakeholders determine whether AI\-enabled capabilities improve network operations, cyber defense, system administration, predictive maintenance, anomaly detection, configuration management, mission planning, or other CANES\-related functions.
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KEY RESPONSIBILITIES:
- Serve as a senior technical advisor for AI evaluation, test planning, performance assessment, and operational suitability analysis in support of Next Generation CANES modernization.
- Develop AI evaluation strategies, test plans, measures of effectiveness, measures of performance, success criteria, risk indicators, and evaluation scorecards.
- Assess AI, machine learning, generative AI, automation, analytics, and decision\-support capabilities for operational relevance, technical maturity, cyber risk, reliability, maintainability, explainability, human oversight, and fleet suitability.
- Evaluate AI\-enabled tools for use cases such as network monitoring, cyber anomaly detection, event correlation, predictive maintenance, help desk automation, configuration compliance, system health monitoring, log analysis, vulnerability prioritization, and operational decision support.
- Design test scenarios that reflect Navy afloat operating conditions, including limited bandwidth, disconnected operations, contested cyber environments, cross\-domain constraints, variable data quality, and platform\-specific operational limitations.
- Define data requirements, ground truth methods, evaluation datasets, labeling approaches, validation methods, and performance baselines for AI\-enabled capabilities.
- Assess AI model performance using appropriate metrics such as accuracy, precision, recall, false positive rate, false negative rate, latency, robustness, drift, confidence calibration, explainability, and operational impact.
- Evaluate risks associated with hallucination, model brittleness, adversarial manipulation, data poisoning, prompt injection, bias, over\-reliance, model drift, cybersecurity exposure, and failure modes in operational environments.
- Support AI red teaming, cyber survivability assessment, adversarial testing, safety reviews, and responsible AI evaluation activities.
- Develop human\-machine teaming concepts, operator\-in\-the\-loop workflows, trust calibration approaches, escalation procedures, and recommended guardrails for AI\-enabled tools.
- Produce technical reports, evaluation findings, executive summaries, test observations, data analysis products, and recommendations for Navy and CACI leadership.
- Collaborate with systems engineers, cybersecurity engineers, software developers, data scientists, network engineers, operational testers, fleet users, and government stakeholders.
- Support technical interchange meetings, design reviews, test readiness reviews, operational assessments, demonstrations, and acquisition decision support.
- Provide SME input on AI governance, responsible AI implementation, model lifecycle management, configuration control, sustainment, monitoring, and continuous evaluation.
REQUIRED QUALIFICATIONS:
- Bachelor's degree in computer science, data science, artificial intelligence, engineering, mathematics, statistics, cybersecurity, operations research, information systems, or a related technical discipline preferred. Advanced degree preferred.
- Additional years of directly relevant AI evaluation, test, cybersecurity, Navy, or DoD mission system experience may be considered in lieu of a degree.
- Demonstrated experience evaluating AI, machine learning, data analytics, automation, or decision\-support systems in defense, intelligence, cybersecurity, network operations, enterprise IT, or mission system environments.
- Strong understanding of AI / ML evaluation methods, test design, performance metrics, validation approaches, model limitations, and operational risk assessment.
- Experience developing test plans, evaluation frameworks, measures of effectiveness, measures of performance, data collection plans, and technical reports.
- Familiarity with cybersecurity, enterprise networks, tactical networks, system monitoring, anomaly detection, log analytics, or network operations use cases.
- Ability to assess AI\-enabled systems in operationally constrained environments, including limited bandwidth, degraded connectivity, edge computing, and mission\-critical infrastructure.
- Understanding of responsible AI concepts, including transparency, explainability, human oversight, robustness, security, bias, accountability, and lifecycle monitoring.
- Experience working with cross\-functional engineering, cyber, data science, software, test, and government stakeholder teams.
- Strong written and verbal communication skills, including the ability to brief complex AI evaluation findings to technical and non\-technical audiences.
- Active DoD Secret clearance.
DESIRED QUALIFICATIONS:
- Experience supporting Navy, DoD, tactical edge, afloat, C4I, cyber, enterprise IT, or mission command systems.
- Familiarity with CANES, Navy afloat networks, ADNS, NAVWAR programs, RMF, cyber survivability testing, operational test, developmental test, or fleet experimentation.
- Experience evaluating generative AI, large language models, retrieval\-augmented generation, autonomous agents, AI\-assisted cyber tools, AI\-enabled network operations, or predictive analytics systems.
- Knowledge of DoD responsible AI guidance, NIST AI Risk Management Framework concepts, RMF, Zero Trust, DevSecOps, MLOps, model monitoring, or secure software supply chain practices.
- Experience with data analysis tools, scripting, statistical evaluation, dashboards, test automation, or model performance analysis.
- Experience with AI red teaming, adversarial ML, cyber test events, operational assessments, or acquisition decision support.
- Top Secret clearance or SCI eligibility.
Requirements of position: Think analytically, effective verbal and written communication skills, make decisions, observe/remember details, interpret data, concentrate on tasks, adjust to change, handle stress/emotions. Regular attendance, maintain work schedule, attend meetings, meet deadlines, keyboard/type, handle confidential information, use math/calculations, stay organized, operate office equipment, may direct others. May be exposed to dust/dirt, humidity, and noise
Foxhole Technology is an Equal Opportunity Employer and makes hiring decisions without regard to race, color, religion, sex (including pregnancy, childbirth and sexual orientation), national origin, age, disability, genetic information, military/veteran status, or any other protected class.
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 Foxhole Technology, 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.
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.
Foxhole Technology AI Hiring
Foxhole Technology has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charleston, SC, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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