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About This Role
Overview:
Millennium is proud to be part of the Markon enterprise, a network of specialized organizations united in support of critical national security missions. This partnership strengthens our ability to deliver results by expanding our technical depth, operational reach, and access to a broader bench of proven experts, ensuring our customers continue to receive best\-in\-class cybersecurity support.
Since 2004, Millennium has operated at the forefront of cybersecurity. Our elite team of over 300 professionals brings an unmatched record of performance across Red Team Operations, Defensive Cyber Operations, Software Engineering, and Technical Engineering. As home to the largest contingent of contracted Red Team operators supporting the Department of Defense, Millennium delivers unparalleled threat intelligence and battle\-tested expertise to both DoD and federal civilian customers.
What We Believe:
Millennium is an equal opportunity employer and does not discriminate or allow discrimination on the basis of race, color, religion, gender, age, national origin, citizenship, disability, veteran status or any other classification protected by federal, state, or local law.
Responsibilities:
Millennium is hiring a skilled AI\-Focused Red Team Operator to support advanced cyber operations for a government customer in the Washington, DC area *(Position contingent upon contract award/additional funding)*. The candidate must have an active Top Secret clearance.
The ideal candidate possesses extensive experience in offensive and defensive cybersecurity operations and is passionate about applying artificial intelligence to improve cyber mission effectiveness. Th candidate will get an opportunity to work at the forefront of AI\-enabled cyber operations, combining traditional penetration testing, adversary emulation, threat hunting, and detection engineering with emerging AI technologies.
- Conduct authorized penetration testing, adversary emulation, and Red Team assessments against enterprise, cloud, and AI\-enabled environments.
- Perform Blue Team activities including threat hunting, detection engineering, incident response support, and defensive security assessments.
- Evaluate AI and machine learning systems for security weaknesses throughout the model lifecycle.
- Assess Large Language Model (LLM) applications against emerging AI threats and industry frameworks such as the OWASP LLM Top 10.
- Identify vulnerabilities involving prompt injection, retrieval\-augmented generation (RAG), model manipulation, indirect prompt injection, tool abuse, data poisoning, and model extraction techniques.
- Develop offensive tradecraft to evaluate AI\-enabled systems while ensuring responsible and authorized testing practices.
- Design and implement defensive controls including guardrails, monitoring, content filtering, evaluation frameworks, and AI\-specific security controls.
- Leverage AI technologies to improve offensive and defensive cyber operations through automation, large\-scale reconnaissance, code analysis, detection engineering, report generation, and security analytics.
- Collaborate with cybersecurity engineers, analysts, and stakeholders to improve organizational resilience against emerging AI\-enabled threats.
- Produce high\-quality technical documentation, assessment reports, and executive briefings.
AI Engineering \& Automation
Successful candidates should demonstrate experience building or integrating AI\-assisted security workflows, including:* Agentic workflows for security operations and incident response
- Automated alert triage and enrichment
- Detection tuning and threat hunting using AI
- AI\-assisted report generation and documentation
- Secure integration of LLMs into cybersecurity workflows
- Evaluation and testing of security\-focused language models
Qualifications:
- Active Top Secret security clearance.
- Bachelor's and total 5\+ years experience. Additonal 4\+ YoE would be considered in lieu of degree.
- Experience conducting Red Team operations, penetration testing, or adversary emulation.
- Design, develop, and evaluate AI agents capable of automating routine penetration testing tasks within authorized environments, employing strict operational guardrails, human oversight, and defined rules of engagement to ensure safe, controlled, and repeatable security assessments.
- Experience supporting Blue Team operations including threat hunting, detection engineering, incident response, or security operations.
- Strong understanding of enterprise networking, operating systems, identity management, cloud technologies, and modern attack methodologies.
- Experience developing scripts or automation using Python, PowerShell, Bash, or similar languages.
- Familiarity with AI/ML concepts, Large Language Models, and AI security risks.
- Strong written and verbal communication skills.
Preferred Qualifications: Experience with one or more of the following:
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- AI Red Teaming
- Prompt injection testing
- Retrieval\-Augmented Generation (RAG) security
- Adversarial machine learning
- Model evasion and data poisoning
- AI governance and secure model deployment
- MITRE ATT\&CK
- MITRE ATLAS
- OWASP LLM Top 10
- NIST AI Risk Management Framework (AI RMF)
One or more of the following certifications are desirable:* CISSP or CEH or OSCP or GPEN or GCIH or PNPT or Security\+ or AI Security coursework or certifications (e.g., AI Security Fundamentals, LLM Red Teaming, or equivalent training)
*
Business Development:
Assist with Business Development activities as required to support Millennium's strategic business objectives, which may include but not limited to participation in technical interviews, creation of technical documentation, general proposal writing support and proposal color reviews.
Physical Requirements:
- Must be comfortable with prolonged periods of sitting at a desk and working on a computer.
- Must be able to lift up to 10\-15 pounds at a time.
COMPENSATION::
$130,000\-$133,000
The salary range provided for this position is intended as a general guideline and does not guarantee a specific salary or compensation amount. Final compensation will be determined based on a variety of factors, including, relevant education, experience, knowledge, skills, and abilities.
Salary Context
This $130K-$133K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Millennium Corporation, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($131K) sits 39% below the category median. Disclosed range: $130K to $133K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Millennium Corporation AI Hiring
Millennium Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $133K - $133K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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