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About This Role
How We're Different
We believe the highest\-impact work in national security and technology is a team sport. At Aperio Global, we operate as a single, cohesive force aligned on a handful of mission\-critical efforts where our work truly moves the needle. We don't chase small wins. We solve for next.
We view the intersection of cybersecurity, artificial intelligence, and data analytics as an empirical discipline as rigorous and consequential as any field in science or engineering. That means we bring intellectual honesty, accountability, and transparency to every problem we take on. Our team includes tech innovators, Intelligence Community veterans, and security professionals who know that the best solutions come from diverse minds working toward a common mission. We value our people, those working quietly behind the scenes, doing the hard work that keeps our nation secure.
The best way to understand how we think is to see what we've built. From full\-spectrum cyber operations and de\-biased AI platforms to quantum networking and cloud\-native software systems, our work speaks for itself.
Come solve with us
We offer competitive compensation and a comprehensive benefits package — including medical, dental, and vision coverage, a 401(k) retirement plan, life insurance, generous paid time off, short and long\-term disability, professional development, and much more. We invest in our people because they are our most important asset — and because the mission demands the best.
\*\*\*\*\*Security Clearance: Active TS/SCI with Polygraph required\*\*\*\*\*
Aperio Global is seeking Senior AI System Engineer to support the exciting Technology \& Tradecraft Integration mission. The Engineer will work closely with mission stakeholders design, integration, and implementation of advanced AI\-enabled solutions, ensuring scalable, secure, and mission\-focused system architectures. This role requires expertise in systems engineering, cloud technologies, data pipelines, analytics, and modern AI capabilities, including Agentic AI workflows and natural language interfaces.
Job Responsibilities/Qualifications:
Mission Focus: Collaborate closely with customers to design and implement AI\-enabled solutions that support data\-driven decision\-making, operational effectiveness, and mission success. Adept at integrating advanced analytics, Agentic AI workflows, cloud technologies, and knowledge management capabilities to transform complex data into actionable intelligence. Proven ability to align technical architectures and system capabilities with evolving mission requirements while ensuring scalability, reliability, and user adoption.
Technical Proficiency: Demonstrated technical proficiency in analyzing complex problems and developing scalable AI\-enabled system architectures that align with mission and business objectives. Experienced in translating conceptual designs into detailed technical specifications while applying systems engineering principles to evaluate system\-wide impacts, dependencies, and performance. Skilled in designing and implementing Agentic AI workflow automations for data pipelines, research, analysis, and reporting, as well as developing intuitive natural language interfaces to knowledge graphs. Proficient in AWS cloud services, data migration to AWS\-hosted databases, advanced analytics, statistical analysis, and the visualization of data science insights to support informed decision\-making.
Qualifications: Bachelor's degree plus 9\-years of relevant experience or equivalent. Proficiency with object\-oriented languages (JAVA, Python, JavaScript) and experience with Jupyter Notebooks, GHOSTMACHINE, data visualization platforms, generative AI, LLM, RAG, Neo4j, AWS (Lambda, EC2, ECS/EKS, SAGEMAKER, S3, RDS, OpenSearch).
Security Clearance: Active TS/SCI with Polygraph required.
Salary Range: 176k\-196k
Benefits
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At Aperio Global, we understand the value of investing in our most important asset—our employees. That's why we have crafted a comprehensive benefits package designed to help you make the best decision for yourself, your family and your lifestyle. For additional details, contact our talent acquisition team.
- Health Care Plan (Medical, Dental \& Vision)
- Retirement Plan (401k, IRA) with 100% employer match up to 6%
- Life Insurance (Basic, Voluntary \& AD\&D)
- Paid Time Off (Vacation, Sick \& Public Holidays)
- Short Term \& Long Term Disability
- Professional Development
- (and much more)
*Aperio Global is an equal opportunity employer. We are committed to building an inclusive workforce where all employees and applicants are treated with respect and fairness. Employment decisions are based solely on qualifications, merit, and business needs — never on race, color, religion, sex, national origin, age, disability, genetic information, military or veteran status, pregnancy/childbirth, sexual orientation, gender identity, marital status, ethnicity, or any other characteristic protected by applicable federal, state, or local law.*
Salary Context
This $176K-$196K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Aperio Global, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $176K to $196K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Aperio Global AI Hiring
Aperio Global has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Meade, MD, US. Compensation range: $196K - $196K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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