Interested in this AI/ML Engineer role at Barbaricum?
Apply Now →About This Role
*Barbaricum is a rapidly growing government contractor providing leading\-edge support to federal customers, with a particular focus on Defense and National Security mission sets. We leverage more than 17 years of support to stakeholders across the federal government, with established and growing capabilities across Intelligence, Analytics, Engineering, Mission Support, and Communications disciplines. Founded in 2008, our mission is to transform the way our customers approach constantly changing and complex problem sets by bringing to bear the latest in technology and the highest caliber of talent.*
*Headquartered in Washington, DC's historic Dupont Circle neighborhood, Barbaricum also has a corporate presence in Tampa, FL, Bedford, IN, and Dayton, OH, with team members across the United States and around the world. As a leader in our space, we partner with firms in the private sector, academic institutions, and industry associations with a goal of continually building our expertise and capabilities for the benefit of our employees and the customers we support. Through all of this, we have built a vibrant corporate culture diverse in expertise and perspectives with a focus on collaboration and innovation. Our teams are at the frontier of the Nation's most complex and rewarding challenges. Join our team.*
Barbaricum is seeking a Senior Data Science Consultant to provide advanced analytical engineering, strategic data architecture, and executive\-level decision\-support modeling. This role serves as a principal technical advisor, focusing on senior analytics, automation, and enterprise platform optimization for the anticipated Headquarters Air Force Mission Assurance (MA) Program. The candidate will lead the design of sophisticated data workflows, predictive reporting tools, and automated monitoring systems to oversee risk management actions for Department of War (DoW) and USAF critical assets in accordance with DoDD 3020\.40 and DODI 3020\.45.
Responsibilities
- Architect enterprise\-level data science workflows and predictive analytics models to provide HAF senior leadership with quantitative foresight into Mission Assurance risks and strategic readiness.
- Lead the technical deployment and optimization of secure data applications, monitoring views, and complex decision\-support tools inside platforms like Palantir Foundry and Advana.
- Design and implement end\-to\-end automation pipelines utilizing advanced machine learning, NLP, and AI methodologies to streamline the ingest and exploitation of unstructured assessment data.
- Construct high\-fidelity risk mitigation and simulation models to mathematically evaluate multiple Courses of Action (COAs) and forecast systemic vulnerabilities across critical military networks.
- Develop sophisticated financial\-to\-operational ROI frameworks that algorithmically cross\-map budget expenditures against Mission Essential Task (MET) readiness and asset protection levels.
- Formulate strategic technical staffing materials, executive dashboards, and white papers to translate complex data science methodologies into actionable insights for general officers.
- Provide authoritative oversight on data governance, ensuring that analytical models and automated reporting systems maintain strict compliance with Joint Staff and Department of the Air Force (DAF) policies.
- Serve as the principal interface between capability providers, senior military leaders, and technical data engineers to align platform architecture with real\-world tactical requirements.
- Direct the technical synthesis of cross\-functional Mission Assurance datasets, standardizing data inputs across MAJCOMs, NAFs, and inter\-service partners.
- Mentor and provide technical oversight to journeyman\-level data analysts and managers, driving best practices in algorithm development and platform application design.
Qualifications
- Clearance: Active DoD TS/SCI Clearance required.
- Education: Master of Arts (MA) or Master of Science (MS) degree in Data Science, Computer Science, Operations Research, Statistics, or a related quantitative field required.
- Experience: Minimum of ten (10\) years of experience in data science, predictive modeling, or advanced analytics, with a proven track record dealing with DoW critical infrastructure or readiness.
- Certifications: Palantir Foundry Application Developer Certification required.
- Passport \& Travel: Valid US passport; ability to travel CONUS or OCONUS approximately 25% of the time.
- Communication: Exceptional oral, written, and briefing skills, with demonstrated experience presenting complex technical/statistical findings directly to flag\-level officers or senior executives.
- Core Skills: Masterful command of advanced analytics, risk management methodologies, problem\-solving under tight timelines, and the ability to independently drive high\-impact technical initiatives.
Desired Qualifications
- Staff Experience: Extensive experience working on large G, J, or A staffs (e.g., Joint Staff, HAF, or MAJCOMs/NAFs) within requirements, plans, and programs or strategic modeling units.
- Domain Expertise: Expert\-level knowledge in a Mission Assurance related discipline including Cybersecurity, Force Protection, Civil Engineering, Intelligence, or Communications.
- Systems Familiarity: Mastery of the Mission Assurance Risk Management System (MARMS), Maven, Envision, or specialized DoD data ecosystems.
EEO Commitment
All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Barbaricum, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Barbaricum AI Hiring
Barbaricum has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.