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About This Role
Herndon, VA
Computer Vision \- Deep Learning/AI
In office
Full\-time
Expedition Technology (EXP) is seeking a Machine Learning Engineer to join a fast\-paced, high\-visibility development program focused on rapidly maturing into an operational capability. In this role, you will design, prototype, and iterate on machine learning models and data pipelines that address real\-world mission problems. The work is focused on temporal, geospatial, and track\-based data, enabling advanced analytics and decision support in complex environments.
This effort is an active development program that must demonstrate measurable progress quickly to enable transition. The ideal candidate is comfortable working in this type of environment: building, testing, and refining approaches under tight timelines while steadily moving capabilities toward production readiness.
We’re looking for engineers who bridge the gap between machine learning research and deployable systems—someone who can experiment, iterate, and incrementally operationalize models in secure, cloud\-native environments.
What You’ll Do
- Design, develop, and deploy machine learning models and pipelines for real\-world mission applications
- Work with temporal and track\-based datasets (e.g., entity tracking, time\-series, geospatial data)
- Build data processing and feature engineering workflows to support model training and evaluation
- Operationalize models using containerized, cloud\-native infrastructure (AWS, Docker, Kubernetes)
- Collaborate with engineers and analysts to translate mission needs into ML\-driven solutions
- Develop and integrate APIs and services that expose model outputs to downstream systems
- Optimize models and pipelines for performance, scalability, and reliability
- Contribute to experimentation frameworks, model evaluation, and continuous improvement workflows
- Participate in Agile development, code reviews, and engineering best practices
Required Qualifications
- U.S. Citizenship
- Active TS/SCI clearance
- 5\+ years of experience in machine learning, data engineering, or backend software engineering
- Strong programming skills in Python
- Experience developing or supporting machine learning models in production environments
- Familiarity with:
- Machine learning frameworks (e.g., PyTorch, TensorFlow, or similar)
- Data processing and analysis (NumPy, Pandas, etc.)
- Understanding of core ML concepts (supervised/unsupervised learning, feature engineering, evaluation)
- Experience with cloud environments (AWS preferred)
- Familiarity with Docker, Kubernetes, or other containerized systems
- Experience working with Linux environments
- Knowledge of Git and modern software development practices (SDLC, CI/CD)
Preferred / Nice\-to\-Have
- Experience working with track, time\-series, or geospatial data
- Familiarity with maritime domain data or analytics
- Understanding of probabilistic modeling, filtering, or tracking algorithms (e.g., Kalman filters, multi\-object tracking)
- Experience building end\-to\-end ML pipelines (data ingestion training deployment monitoring)
- Exposure to distributed data processing frameworks
- Experience deploying ML systems in classified or mission environments
Who is Expedition Technology?
Expedition Technology designs, develops, and delivers innovative, advanced signal, image, and multi\-INT solutions for the defense and intelligence communities. We leverage advanced algorithms, platforms, and technologies to solve our customers’ most complex, demanding, and urgent C4ISR challenges. Our culture promotes individual growth and opportunity, prioritizes a collaborative team spirit, and invites the intellectually curious to creatively solve challenging problems. Headquartered in Northern Virginia’s high\-tech corridor, EXP is a rapidly growing, privately held, employee\-owned company that pushes the boundaries of what is possible every day.
Interested in joining our team? Let’s explore together.
To learn more about EXP and discover why we are an award\-winning workplace, visit our web site and follow us on LinkedIn.
What do we offer our team?
Expedition Technology (EXP) offers a flexible, self\-directed benefits package that is designed to fit your individual needs. Benefits include:
- Company\-paid, medical, dental and vision insurance
- Up to 45 days of PTO
- 12% 401k match \- Traditional and Roth options available
- Student loan repayment assistance
- Paid Family Leave
- Tuition Reimbursement \- $5250/year available
- Referral bonus program
- Free tickets to sporting events, theater, concerts and more
- Free, onsite fitness center, onsite cafeteria with reduced\-cost meals
- A collaborative, creative and supportive culture where you will be encouraged to push boundaries, take risks and enjoy the rewards.
*EXP is proud to be an Equal Opportunity Employer that believes a diverse range of talent creates an environment that fosters creativity and innovation*. *All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, disability, national origin, genetic information, or protected veteran status.*
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 Expedition Technology, 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.
Expedition Technology AI Hiring
Expedition Technology has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Herndon, VA, US.
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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