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About This Role
What Impact You'll Have
GRVTY's team provides tactical data engineering solutions. We embed skilled Data Engineers, Data Scientists, and ETL Developers directly into intelligence analyst groups to be their go\-to data wranglers. We develop new tools, code, and services to execute data engineering activities. Our engineers work to collect, process, and feed analytic tools, turning data into intelligence in response to immediate mission needs, with direct impact on real world situations. You will see your work used here on a daily basis, and you'll have the opportunity to support a variety of Sponsor mission organizations and mission partner organizations. This is a time of development and growth on the program, with an increasing number of missions being supported. The work is high impact and important, and the customer moves quickly. The environment is fast\-paced, flexible, and open to innovation \- you'll have more latitude here in choosing how to achieve results than on many other projects. The customer cares more about what you can do as opposed to your years of experience, and work hours are typically quite flexible \- roll up your sleeves, get things done, and no one cares much about the specific hours that you work. The work space itself is also quite nice, and there is an excellent cafeteria! Work on this program takes place in McLean, VA and in various field offices throughout Northern VA (we cannot support remote work) and requires a TS/SCI \+ Polygraph clearance (acceptable to this customer).
What You'll be Owning
GRVTY is seeking an experienced AI/ML Engineer with a TS/SCI \+ Poly clearance (applicable to this customer) to join one of our top projects in McLean, VA.
- Implementing and maintaining RAG pipelines, including document processing, embedding generation, retrieval configuration, and prompt assembly
- Integrating LLMs into applications using available APIs and frameworks
- Developing and maintaining REST API interactions to support data retrieval and system integration
- Designing and/or refining Postgres schemas to improve data organization and query performance
What You Must Have
- Active TS/SCI with Polygraph Clearance
- Minimum 8 years of experience with a Bachelor's degree; or 7 years of experience with a Masters degree; or 6 years of experience with a Doctorate
- Demonstrated ability to conduct independent technical research, evaluate emerging AI/ML approaches, and apply advanced analytical problem\-solving comparable to PhD\-level research environments
- Ability to rapidly learn and apply new AI/ML methodologies, tools, and frameworks in support of evolving mission requirements
- Experience developing AI/ML applications focused on Retrieval\-Augmented Generation (RAG), semantic retrieval, LLM integration, or related AI workflows
- Strong proficiency in Python and modern AI/ML libraries, frameworks, and API integrations
- Willingness to work onsite full time; 40 hours, 5 days a week (Monday \- Friday), and covering at a minimum the Sponsor's core office hours of 10AM\-2PM
What Would be Nice to Have
- Advanced research experience in machine learning, deep learning, natural language processing, generative AI, reinforcement learning, computer vision, or related disciplines.
- Experience publishing research, contributing to open\-source AI/ML initiatives, or leading experimental and prototype development efforts.
- Familiarity with model evaluation frameworks, fine\-tuning workflows, inference optimization, and AI observability/monitoring tools.
- Experience with vector databases, AWS/cloud environments, Docker, and containerized AI/ML development workflows.
- Experience designing and integrating REST APIs and scalable data architectures.
\#LI\-BPJ
Why Choose GRVTY
The toughest national security challenges demand vision and ingenuity, not just resources. We deliver mission and technical expertise to outpace our adversaries. We're purpose\-built to tackle the most entrenched, systemic national security issues around the world.
We partner with our customers to help them overcome challenges in every corner of technology and defense—including the ones still being explored. Our growing capabilities create complementary advantages, giving on\-the\-ground operations the edge they need to succeed. We muster everything we have to answer every challenge presented, every day of our lives.
At GRVTY, we believe that when our employees thrive, our company thrives. That's why we offer a comprehensive and competitive benefits package designed to support your well\-being, growth, and work\-life balance.
- Robust health plan including medical, dental, and vision
- Health Savings Account with company contribution
- Annual Paid Time Off and Paid Holidays
- Paid Parental Leave
- 401k with generous company match
- Training and Development Opportunities
- Award Programs
- Variety of Company Sponsored Events
EEO Statement
GRVTY, 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, disability, or status as a protected veteran and will not be discriminated against on the basis of disability.
Anyone requiring reasonable accommodations should email [email protected] or call 703\-544\-7930 with requested details. A member of the HR team will respond to your request within 2 business days.
Know Your Rights: Workplace Discrimination is Illegal (eeoc.gov)
Please review our current job openings and apply for the positions you believe may be a fit. If you are not an immediate fit, we will also keep your resume in our database for future opportunities.
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 GRVTY, 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.
GRVTY AI Hiring
GRVTY has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Chantilly, VA, US, Laurel, MD, US, McLean, VA, US. Compensation range: $175K - $175K.
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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