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About This Role
##### About Peraton
Peraton is a next\-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world’s leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees solve the most daunting challenges that our customers face. Visit peraton.com to learn how we’re keeping people around the world safe and secure.
##### About The Role
Next\-Gen AI/ML Systems Engineer \- Data Fusion \& Sense\-Making
Within Peraton's Space and Mission Solutions sector, there is an immediate need for a cleared AI (Artificial Intelligence) Data Fusion \& Sense\-Making emergent technologist to advance our artificial intelligence and data analytics capabilities across the US Intelligence and Space Community portfolio. This is a full\-time, on\-site position based in Reston, VA with the ability to support from other Peraton facilities in Chantilly, Reston, and/or Herndon with flexibility to travel to customer sites for classified and non\-classified solution activities.
As a Next\-Gen Technology Engineer (NTE), reporting directly to the Sector CTO, you will be the organization's expert on leveraging artificial intelligence to transform disparate data sources into actionable intelligence. You will bring a broad, strategic view of AI applications, understanding not just what AI can do, but where and how it should be applied to maximize mission impact. Your expertise in AI testing, validation, and responsible AI practices will ensure our solutions are trustworthy and operationally effective.
This role is designed for professionals who combine deep AI/ML (Artificial Intelligence/Machine Learning) technical skills with practical defense sector experience. You understand that AI adoption in government requires more than technical excellence—it demands rigorous testing, explainability, and alignment with customer missions and constraints.
What you’ll do:
- Lead the design of AI\-enabled data fusion solutions that integrate multi\-source intelligence (SIGINT, GEOINT, HUMINT, OSINT, etc) into coherent, actionable insights for government proposals
- Develop AI/ML architectures for data management, optimization, and sense\-making that address customer\-specific mission requirements
- Establish and apply AI testing and validation frameworks to ensure solution reliability, explainability, and compliance with responsible AI principles
- Maintain broad awareness of AI landscape—from foundation models to specialized ML techniques—and translate capabilities into practical mission applications
- Create AI adoption roadmaps that account for customer readiness, data maturity, and organizational change management requirements
- Collaborate with business development, capture teams, and program managers to identify AI opportunities and develop differentiated technical solutions
- Infuse existing programs with AI capabilities where appropriate, working within operational and security constraints to demonstrate value incrementally
- Mentor, train, and/or educate technical staff on AI/ML fundamentals, testing practices, and application strategies, building organizational AI literacy
- Articulate AI solutions, their limitations, and mission impact to senior leadership, customers, and governance review boards
##### Qualifications
Required Qualifications:
- A minimum of 5\+ years of experience with a BS/BA in Computer Science, Data Science, Mathematics, or related field; or 3\+ years with MS/MA in Computer Science, Data Science, Mathematics, or related field; or 1\+ PhD with relevant AI/ML research.
- This position requires the candidate to possess a minimum of Secret clearance with the ability to obtain TS/SCI. The candidate must maintain the clearance.
- Demonstrated expertise in AI/ML techniques for data fusion, integration, or analytics—including experience with multiple data modalities
- Hands\-on experience with AI testing, validation, and evaluation methodologies
- Broad understanding of AI landscape including supervised/unsupervised learning, deep learning, NLP, computer vision, and foundation models
- Working knowledge of data management practices, data pipelines, and data quality considerations for AI systems
- Exposure to defense or intelligence community environments and understanding of unique AI adoption challenges in classified settings
- Strong written and verbal communication skills with ability to explain AI concepts and limitations to non\-technical audiences
- Pragmatic approach to AI—understanding that not all problems require AI solutions and that customer readiness varies significantly
- Full\-time on\-site availability with ability to collaborate across technical, business development, and program teams
Desired Qualifications:
- An active/current TS/SCI is highly desired
- Direct experience supporting NRO, NGA, USSF, DIA, NASA, or NOAA missions and programs
- Knowledge of responsible AI frameworks, AI ethics, and bias mitigation techniques
- Familiarity with MLOps practices, model monitoring, and AI system lifecycle management
- Experience with AI development platforms and frameworks (PyTorch, TensorFlow, Hugging Face, etc.)
- Relevant certifications or credentials in AI/ML, data science, or analytics
- Experience contributing to proposals with AI/ML technical components
- Graduate coursework or research publications in AI/ML, data fusion, or related fields
Benefits:
At Peraton, our benefits are designed to help keep you at your best beyond the work you do with us daily. We’re fully committed to the growth of our employees. From fully comprehensive medical plans to tuition reimbursement, tuition assistance, and fertility treatment, we are there to support you all the way.
\#SPACENEXTGEN
##### Details
Target Salary Range: $104,000 \- $166,000\. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
Benefits Statement: Peraton offers eligible employees a variety of benefits including medical, dental, vision, life, health savings account, short/long term disability, EAP, parental leave, 401(k), paid time off (PTO) for vacation, and company paid holidays. A full listing of available benefits can be viewed at https://www.careers.peraton.com/benefits.
Application Statements: The application period for the job is estimated to be 30 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates. By applying to this job, you are expressing interest in the role and the Company. During the review of your application, you may be required to participate in an on\-camera interview, as well as participate in a process to verify your identity.
EEO: Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.
Salary Context
This $104K-$166K range is in the lower quartile 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 Peraton, 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 ($135K) sits 25% below the category median. Disclosed range: $104K to $166K.
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.
Peraton AI Hiring
Peraton has 28 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, Data Scientist. Positions span Reston, VA, US, Laurel, MD, US, Herndon, VA, US. Compensation range: $128K - $234K.
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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