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About This Role
About Agile Defense
At Agile Defense we know that action defines the outcome and new challenges require new solutions. That’s why we always look to the future and embrace change with an unmovable spirit and the courage to build for what comes next.
Our vision is to bring adaptive innovation to support our nation's most important missions through the seamless integration of advanced technologies, elite minds, and unparalleled agility—leveraging a foundation of speed, flexibility, and ingenuity to strengthen and protect our nation’s vital interests.
Requisition \#: 1745
Job Title: Sales Engineer, Embedded AI
Location: Hybrid, McLean, VA
Clearance Level: Top Secret, Must Have Ability to Obtain a Clearance
### Job Description
Agile Labs is the applied\-AI division of Agile Defense. We build non\-exquisite AI systems and field them across cloud, on\-premises, and air\-gapped environments for the Army, Air Force, Navy, Marine Corps, FBI, SOCOM, and other mission\-driven customers. We do this through three integrated teams that operate as a flywheel: Embedded AI proves the need inside operational units, Force\-Deployed Engineering proves it works in the field, and Product hardens what works into reusable capability.
The Embedded AI team is the customer\-depth team. We embed where decisions actually get made, find the mission problems that do not yet have a repeatable solution, and prototype against them fast using our platform stack. The Sales Engineer is the technical voice in that room — the person who can sit across from an operator or program office, understand the problem in their language, and translate it into a working demonstration of capability inside their mission context. You are the bridge between what the customer needs and what our stack can do, and you carry that translation all the way from first conversation to fielded proof.
This is a hands\-on, technical, customer\-facing role. It is not a quota\-carrying closing seat and it is not a back\-office solutions desk. You will spend your time in front of customers and inside our own platform, and you will be measured on whether the capability you put in front of a customer earns the next engagement.### Education and Background
College degree in computer science or relevant field preferred.### Years of Experience
4\+ years### Required Skills
- 4\+ years in a sales engineering, solutions architecture, forward\-deployed engineering, or technical pre\-sales role — ideally serving DoD, IC, or federal civilian customers.
- The ability to operate at the intersection of deep technical detail and commercial/strategic reasoning, and to move fluidly between an operator, a program manager, and an engineer in the same day.
- Hands\-on technical fluency: comfort building and demonstrating software, working with APIs and integrations, and reasoning about modern AI systems (agents, orchestration, RAG, evaluation) well enough to design a credible demo and defend it under questioning.
- Strong discovery and requirements instincts — you ask the questions that help a customer think, not the ones that extract free labor from them, and you can write clear functional and non\-functional requirements from a messy conversation.
- Excellent written and verbal communication, including the ability to produce briefs and proposal content that hold up to technical scrutiny.
- Active TS/SCI clearance, active Secret with the eligibility and willingness to upgrade, or ability to obtain either.
- Willingness to travel to customer and field sites.
### Preferred Skills
- Direct experience with SOF, SOCOM, or Combatant Command customers, or with the FBI / IC.
- Familiarity with the federal acquisition landscape: SWP / Software Acquisition Pathway, OTAs, IDIQ vehicles, and the realities of capture and proposal cycles.
- Working knowledge of accreditation and compliance frameworks relevant to fielding AI in government environments (IL\-5, FedRAMP, CMMC, NIST, cATO, OSCAL).
- Experience building or demonstrating agentic AI, OSINT, C2, or risk\-screening capabilities.
- A background that combines technical delivery with business development or capture instincts — the rarer the combination, the better the fit.
### Working Conditions
Office EnvironmentOur Core Values
Employees of Agile Defense are our number one priority, and the importance we place on our culture here is fundamental. Our culture is alive and evolving, but it always stays true to its roots. Here, you are valued as a family member, and we believe that we can accomplish great things together. Agile Defense has been highly successful in the past few years due to our employees and the culture we create together.
What makes us Agile? We call it the 6Hs, the values that define our culture and guide everything we do. Together, these values infuse vibrancy, integrity, and a tireless work ethic into advancing the most important national security and critical civilian missions. It's how we show up every day. It's who we are.
- Happy \- Be Infectious. Happiness multiplies and creates a positive and connected environment where motivation and satisfaction have an outsized effect on everything we do.
- Helpful \- Be Supportive. Being helpful is the foundation of teamwork, resulting in a supportive atmosphere where collaboration flourishes, and collective success is celebrated.
- Honest \- Be Trustworthy. Honesty serves as our compass, ensuring transparent communication and ethical conduct, essential to who we are and the complex domains we support.
- Humble \- Be Grounded. Success is not achieved alone, humility ensures a culture of mutual respect, encouraging open communication, and a willingness to learn from one another and take on any task.
- Hungry \- Be Eager. Our hunger for excellence drives an insatiable appetite for innovation and continuous improvement, propelling us forward in the face of new and unprecedented challenges.
- Hustle \- Be Driven. Hustle is reflected in our relentless work ethic, where we are each committed to going above and beyond to advance the mission and achieve success.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $125K-$155K range is below the median 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 Agile Defense, 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 ($140K) sits 35% below the category median. Disclosed range: $125K to $155K.
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.
Agile Defense AI Hiring
Agile Defense has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McLean, VA, US. Compensation range: $155K - $155K.
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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