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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
Peraton is seeking a UX Analyst to support the DFAS environment by delivering modern, user-centered digital experiences across web and mobile applications.
This is a hands-on UX role focused on moving rapidly from requirements and user needs to high-fidelity, interactive experiences. The UX Analyst will use Figma, AI-assisted design and research tools, design systems, rapid prototyping, usability validation, and accessibility practices to transform complex requirements into intuitive, development-ready solutions.
The ideal candidate is comfortable working directly within Agile product teams and can use AI to accelerate—not replace—UX thinking, research synthesis, design exploration, documentation, accessibility evaluation, and prototyping.
The role requires someone who can take an experience from concept to high-fidelity prototype to development handoff, while collaborating closely with product owners, stakeholders, developers, and end users.
This position is 100% remote.
Responsibilities:
- Support the full UX lifecycle from discovery, requirements analysis, and user research through high-fidelity design, interactive prototyping, validation, development handoff, and implementation review.
- Translate business, mission, technical, and user requirements into clear user journeys, task flows, experience maps, information architecture, and interface solutions.
- Create high-fidelity Figma screens and interactive prototypes that accurately demonstrate intended workflows, interactions, responsive behavior, states, and user outcomes.
- Use AI-assisted UX workflows to accelerate research synthesis, requirements analysis, concept exploration, content development, accessibility review, prototyping, and design iteration.
- Apply strong UX judgment to validate and refine AI-generated concepts rather than relying on AI-generated output without human review.
- Develop and maintain Figma component libraries, variables, reusable patterns, templates, and design-system components that promote consistency and accelerate delivery.
- Create UX artifacts as appropriate, including user journeys, personas, workflows, information architecture, experience maps, wireframes, high-fidelity mockups, prototypes, storyboards, and design specifications.
- Conduct and synthesize user research and stakeholder discovery through interviews, surveys, observation, contextual inquiry, usability testing, heuristic evaluations, and other appropriate research methods.
- Plan, facilitate, and analyze usability testing using interactive prototypes and working applications; translate findings into actionable design recommendations.
- Rapidly iterate concepts using high-fidelity prototypes and AI-enabled prototyping techniques to allow stakeholders and users to experience proposed solutions before development investment.
- Design responsive experiences for desktop, tablet, and mobile environments, considering device capabilities, user context, interaction methods, and platform conventions.
- Ensure designs meet applicable Section 508 and WCAG accessibility requirements, incorporating accessibility into the design process rather than treating compliance as a final review activity.
- Design accessible interaction patterns including keyboard navigation, focus behavior, semantic structure, form interactions, error handling, color contrast, responsive behavior, and assistive-technology considerations.
- Apply established organizational design systems and standards, including U.S. Web Design System (USWDS) patterns when applicable.
- Collaborate directly with product managers, business analysts, developers, architects, accessibility specialists, and stakeholders to ensure proposed experiences are both desirable and technically feasible.
- Provide development-ready Figma specifications and assets, including responsive behavior, component states, interaction logic, icons/SVGs, spacing, sizing, and design-system references.
- Work closely with front-end developers during implementation to ensure the delivered experience accurately reflects the approved UX direction.
- Conduct UX implementation reviews and identify discrepancies between approved designs and developed interfaces.
- Participate in Agile/Scrum ceremonies including planning, refinement, reviews, demonstrations, retrospectives, and UX/design critiques.
- Clearly communicate and present design rationale, research findings, prototype concepts, tradeoffs, and recommendations to technical and non-technical stakeholders.
The UX Analyst should demonstrate the ability to responsibly incorporate modern AI capabilities into the UX workflow.
This may include using AI to:
- Analyze and synthesize research, stakeholder feedback, and requirements.
- Identify themes, usability issues, gaps, and potential experience opportunities.
- Explore multiple interaction and experience concepts rapidly.
- Generate initial content, interface concepts, or design alternatives for human evaluation.
- Accelerate creation of prototypes and proof-of-concept experiences.
- Assist with accessibility analysis and design-system compliance.
- Support UX documentation, user stories, acceptance criteria, and development handoff.
- Translate design intent into prototype code or front-end concepts when appropriate.
Qualifications
Required Skills:
- Bachelor’s degree in Human-Computer Interaction, UX Design, Interaction Design, Information Technology, Computer Science, Digital Design, Psychology, or a related field, or equivalent relevant professional experience with a minimum of 4 years of progressive professional UX, product design, interaction design, usability, or related digital experience demonstrating the required capabilities.
- Must be able to obtain and maintain a Public Trust.
- U.S. Citizenship required.
- Professional UX certification such as Nielsen Norman Group (NN/g), Human Factors International (HFI), or equivalent is preferred where required by the program.
- Demonstrated portfolio of professional work showing high-fidelity interface design, interactive prototypes, user flows, design systems, responsive experiences, and implemented digital products.
- Strong experience creating high-fidelity UI designs and interactive prototypes in Figma.
- Demonstrated ability to translate ambiguous or complex requirements into intuitive digital experiences.
- Strong understanding of user-centered design, interaction design, information architecture, usability, and human-computer interaction principles.
- Experience conducting or supporting user research and usability testing.
- Experience using AI-assisted tools within UX, research, design, or prototyping workflows.
- Ability to create responsive web and mobile experiences using modern design patterns and component-based design systems.
- Working knowledge of Section 508 and current WCAG accessibility practices.
- Experience working with design systems and reusable component libraries.
- Ability to clearly articulate design decisions and present UX concepts to customers, leadership, developers, and other stakeholders.
- Strong collaboration skills within multidisciplinary Agile product teams.
- Ability to balance user needs, mission objectives, technical constraints, accessibility requirements, and delivery timelines.
- Understanding of HTML, CSS, JavaScript, and modern front-end development concepts sufficient to effectively collaborate with engineering teams and evaluate implementation feasibility.
Preferred Skills:
- Experience with Figma variables, component properties, Auto Layout, responsive design, interactive components, Dev Mode, and advanced prototyping.
- Experience with AI-enabled prototyping, design-to-code, or rapid application development tools.
- Experience designing within USWDS or other enterprise/government design systems.
- Experience supporting federal government digital products or other complex enterprise environments.
- Familiarity with accessibility evaluation tools and automated accessibility testing.
- Experience designing applications that support complex workflows, data entry, dashboards, case management, or transactional processes.
- Familiarity with front-end frameworks and component-based development approaches.
- Ability to facilitate collaborative workshops, design reviews, stakeholder sessions, and UX discovery activities.
Details
Target Salary Range: $80,000 - $128,000. 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. Use of artificial intelligence (AI) tools of any kind during Peraton interviews is strictly prohibited unless the candidate has obtained prior written authorization. All interview responses must be the candidate’s own.
EEO: Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.
Salary Context
This $80K-$128K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($104K) sits 51% below the category median. Disclosed range: $80K to $128K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Peraton AI Hiring
Peraton has 16 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, Research Scientist, Data Scientist. Positions span Albany, NY, US, Laurel, MD, US, College Park, MD, US. Compensation range: $128K - $304K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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