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About This Role
About Us:
VT-ARC, a technical services and applied research company, has built an organizational culture marked by four primary values: Teamwork, Integrity, Excellence, and Service. Integral to our success is our staff’s enthusiasm for solving tough problems by working together in teams to get the job done. We foster a culture where every employee’s contribution is valued and performed with integrity while maintaining a fun work environment. VT-ARC strives for excellence in all that is done for our clients, and such achievement is recognized through service/merit awards. Moreover, we promote a sense of community larger than VT-ARC alone, where staff and institutional resources can be applied in service to our country. We are proud to be the recipient of the Best Workplace in Defense Award by Emergent Magazine, an honor that recognizes companies with positive cultures that not only impact their people but also make a meaningful difference in the community.
About You:
You are a doctoral-trained researcher and systems thinker with expertise in modeling, simulation, or a related engineering discipline. You enjoy turning open-ended questions into testable models and working prototypes. You bring sound research judgment, hands-on coding ability, and curiosity about how people, technologies, decisions, and operational constraints interact. You are a self-starter who can propose an approach, write and test code, investigate unexpected results, and communicate what the evidence supports. You work independently on agreed research tasks while seeking mentorship and responding constructively to review. You want to extend your doctoral expertise into mission engineering and analytical wargaming, develop government-facing communication skills, and explore new ideas with experienced researchers and engineers.
Position Overview:
VT-ARC is seeking a Postdoctoral Research Fellow - Agentic Modeling, Simulation & Wargaming for a paid, mentored research appointment within DSD. This opportunity focuses on generative agent-based simulation, human-supervised agentic analytical workflows, and decision-support prototyping, with applications to mission engineering, logistics, mobilization, industrial capacity, and other agreed research topics.
Working with an assigned senior research mentor, the fellow will formulate research questions, design experiments, and independently code, test, and demonstrate focused proofs of concept using approved unclassified information. The appointment combines original research with development in systems engineering, mission analysis, and sponsor engagement. Prior defense employment, an existing clearance, and independent wargame leadership are not prerequisites; however, familiarity with the National Security space and/or a keen interest are critical to for the long-term success of the candidate.
The fellowship includes dedicated research exploration and values reproducible methods and well-supported negative findings. Outputs may include models, prototype software, technical papers, and presentations, subject to release approval. Production engineering, operational support, and responsibility for entire sponsor programs are outside the baseline scope; promising prototypes may transition to additional developers and specialists.
This position is a contingency hire based on a contract award expected to begin execution on or about November 01**st*, 2026.*
Duties/Responsibilities:
- Work with an assigned senior research mentor to develop research questions, hypotheses, evaluation criteria, and a feasible work plan; propose original investigations and contribute to an individual development plan.
- Design, code, test, debug, document, and demonstrate prototype models, simulation components, data integrations, and analytical applications. Take a bounded concept through implementation and iterative evaluation.
- Develop and evaluate generative agent-based simulations and human-supervised agentic workflows, including agent behavior, task decomposition, tool use, constraints, logging, and human review.
- Design experiments and sensitivity studies; assess effects of prompts, model selection, stochastic variation, data quality, and assumptions. Compare results with baselines or reference cases and document limitations.
- Use Python and supporting development tools for data preparation, APIs, simulation execution, and analysis. Use approved AWS or comparable environments within agreed access controls and compute budgets.
- With engineering mentorship, translate research into mission threads, conceptual models, requirements, interfaces, and evaluation plans; preserve traceability from assumptions and implemented logic to findings.
- Contribute scenarios, evaluation logic, data collection, and prototype demonstrations to analytical wargames or tabletop studies, with guidance from experienced game designers and mission specialists.
- Participate in approved, unclassified discussions with government stakeholders; ask clarifying questions, demonstrate technical work, and brief findings and limitations with increasing independence.
- Maintain version-controlled code, tests, experiment records, and technical documentation. Prepare research briefs, presentations, and manuscripts or publication-quality material where release is authorized.
- Manage agreed tasks and milestones, report progress, identify risks early, and collaborate in technical reviews. Use approved coding assistants responsibly and independently verify generated code when such tools are used.
Required Education, Certification, Skills, Capabilities:
- Demonstrated research and modeling capability through doctoral work, publications, research engagements, or substantial technical projects. No minimum years of post-Ph.D. employment are required.
- Ability to build, test, debug, and explain functioning analytical software or research prototypes. Working Python proficiency, or strong proficiency in another relevant language with demonstrated ability to become productive in Python quickly.
- Experience with agent-based modeling, system dynamics, generative agents, or related computational modeling; working understanding of agentic AI and ability to investigate and verify model behavior.
- Ability to design experiments, evaluate evidence, conduct sensitivity analysis, and maintain reproducible code and research records; practical familiarity with APIs, version control, debugging, and testing.
- Experience using cloud tools such as AWS, or demonstrated ability to learn approved environments quickly while following data-handling, access, and resource-use requirements.
- Ability to structure open-ended problems, learn new mission domains, communicate clearly with technical and nontechnical audiences, and independently execute agreed work while responding constructively to review.
- Completed Ph.D. in Industrial and Systems Engineering, another engineering discipline, Operations Research, Computer Science, Applied Mathematics, or a related technical field by the fellowship start date. Doctoral research and relevant project work count as experience.
Desired Education, Certification, Skills, Capabilities:
- Experience with large language model agents, multi-agent simulations, prompt or model sensitivity, uncertainty analysis, and evaluations of AI-generated behaviors or outputs.
- Experience using AI coding assistants or agentic development tools for prototyping, testing, debugging, and documentation, with independent verification and appropriate protection of code and data.
- Experience with cloud-hosted prototypes, automated data pipelines, databases, APIs, interactive applications, or simulation/game software.
- Familiarity with systems or mission engineering, requirements, model-based systems engineering, analytical wargaming, or national security applications. Prior employment in these areas is not required.
- Experience communicating research to external partners, preparing publications or proposals, collaborating across disciplines, or demonstrating working capabilities.
Postdoctoral Research & Professional Development: The fellow and an assigned senior research mentor will establish a written research and individual development plan with milestones and regular technical and career-development reviews. The plan includes research exploration, systems/mission engineering and wargaming development, and authorized publications, presentations, and government engagement. Approved research and professional-development activities are part of the paid work plan. This fixed-term appointment may be extended subject to funding, performance, and HR approval. It can build qualifications for future VT-ARC research or systems/mission engineering positions. Any subsequent role requires an available opening, funding, demonstrated qualifications, applicable access requirements, and the appropriate hiring process; conversion or a particular level is not guaranteed.
Primary Work Location: Arlington, VA / National Capital Region; U.S. based remote work may be considered and approved for this position. However, location and onsite attendance will be agreed before appointment, subject to HR, security, and program requirements.
Special Work Conditions: Limited, preapproved travel may be needed for research collaboration, demonstrations, workshops, or professional development. Scope, schedule, and authorized work hours will be agreed in advance. Approved research tools and compute resources will be provided or funded by VT-ARC.
Security: An active security clearance is not required at the time of hire. Fellowship work will be limited to approved unclassified information and authorized systems. All work remains subject to applicable data-handling, privacy, export-control, and program-access requirements. However, the selected candidate must meet employment authorization and applicable background-screening requirements. Clearance sponsorship will be pursued subject to authorized sponsorship and government adjudication; clearance or a specific processing timeline is not guaranteed.
Competitive Salary:Proposed compensation is $45 - $55 hourly. Work time will be recorded and compensated under applicable requirements, and availability of hours is subject to the availability of direct work. Weekly hours, appointment duration, benefits eligibility, and overtime treatment will be specified in the offer.
Virginia Tech Applied Research Corporation: VT-ARC is a 501(c)(3), non-profit R&D organization affiliated with Virginia Polytechnic Institute and State University (Virginia Tech or VT). Our mission is to provide superior analytic and technology solutions across multiple domains by leveraging Virginia Tech’s multidisciplinary research and innovation ecosystem. With unique access to the broad and rich research enterprise found at Virginia Tech, VT-ARC forms multi-disciplinary teams to apply innovative solutions to the real-world problems that strain our social, political, industrial, and economic foundations.
To learn more about VT-ARC’s Benefits, Perks, Culture & more visit our Careers page: https://vt-arc.org/careers/
Virginia Tech Applied Research Corporation is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by law. As a federal contractor, we are committed to providing equal employment opportunity and affirmative action for qualified individuals with disabilities under Section 503 of the Rehabilitation Act of 1973. If you need a reasonable accommodation to complete the application or interview process, please contact Human Resources at [email protected]
Virginia Tech Applied Research Corporation uses E-Verify to confirm the employment eligibility of all newly hired employees. To learn more about E-Verify, including your rights and responsibilities, please visit www.E-Verify.gov*.*
Salary Context
This $93K-$114K 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 Virginia Tech Applied Research 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 $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 ($104K) sits 51% below the category median. Disclosed range: $93K to $114K.
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.
Virginia Tech Applied Research Corporation AI Hiring
Virginia Tech Applied Research Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $114K - $114K.
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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