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About This Role
Title: AI/ML Engineer
Location: Herndon, Virginia
\*Clearance: \*Active TS/SCI w/ Polygraph needed to apply \*
Company Overview:
*Cornerstone Defense is the Employer of Choice within the Intelligence, Defense, and Space communities of the U.S. Government. Realizing early on that our most prized assets are our employees, we continually focus our attention on improving the overall work/life experience they have supporting the mission. Our Team is pushed every day to use their industry leading knowledge to provide end\-to\-end solutions to combat our nation’s toughest and most secure problems. If you are looking for a place to not only be professionally challenged, but encouraged and supported by a company that cares, don’t look any further than Cornerstone Defense.*
Currently seeking a Software Engineer (Expert) in Herndon VA to work on a suite of services that will supply users with tools to automate a wide range of enterprise\-wide applications and data centric mission management applications, with a focus on artificial intelligence and machine learning (AI/ML). The candidate will be part of a team of Software Developers, ETL Developers, and Data Scientists to enhance existing production applications and build out new web services on a routinized production cadence to meet the evolving needs of users. This role is focused on developing high\-performance, distributed systems that operate on petabyte\-scale structured and unstructured data, enabling mission users to rapidly access, process, and exploit critical information.
You will operate at the intersection of big data, distributed systems, and mission analytics, building core platform capabilities such as ingest pipelines, text extraction services, and search infrastructure that directly enable downstream analytics, AI, and operational workflows.
This is a hands\-on technical leadership role for engineers who thrive on building scalable systems from the ground up, solving hard data problems, and delivering production\-grade capabilities that operate in high\-stakes, real\-world environments.
This position will include a variety of activities, including:
Integrating AI/ML technology into a production web application.
Participation with iterative software development teams with adherence to all reporting requirements.
Designing, developing and unit testing code for a production system and demonstration capabilities.
Developing rapid prototypes to drive out requirements and design.
Providing demonstrations and detailed walk through of features to a variety of technical and non\-technical audiences.
Meeting with stakeholders, analyzing requirements, developing user stories, and translating these into software development tasks.
Development of technical documentation and briefing materials to support program status reviews, control gates, and other presentations as directed.
Write and maintain technical documentation for application workflows, integrations, and compliance procedures.
Optimize and enhance application performance, ensuring scalability and security.
Demonstrated 4\-6 years in backend development – must be proficient in both Python and Java
Demonstrated 4\-6 years in frontend development like Angular
Demonstrated 6\-8 years of AWS Architecture design proficiency
Significant proficiency 6\-8 years in Linux – shell scripting, mounting drives, text editing with vi or emacs, system diagnostic checks \[memory management, process running]
Demonstrated 2\-3 years working with AI/ML development and data science understanding
Demonstrated experience on Agile software development teams following Agile methodologies, including Scrum and Kanban.
Demonstrated experience using Jira, Confluence, and GitHub for documenting work and team collaboration.
Minimum 6\-8 years of experience with SQL and NOSQL databases
Minimum 6\-8 years of demonstrated experience with development and deployment of applications in the Commercial Cloud Services (C2S) environment or an Amazon Web Services cloud environment.
Minimum 6\-8 years of demonstrated experience with search \& analytic tools. Examples to include ElasticSearch, Solr/Lucene, OpenSearch.
Minimum 2\-4 years of demonstrated (Extract, Transform, Load \- ETL) with large structured and unstructured raw data sets.
Minimum 6\-8 years of demonstrated ability to design, develop, test and implement new applications based on project requirements.
The ability to work individually as well as the ability to work in project teams. Proven ability in decomposing concepts to discrete development tasks and managing your work to a deadline.
Minimum 6\-8 years of demonstrated ability in microservices up to and including docker, podman, and or containerization services.
Minimum 6\-8 years in Object Oriented programming. Python is preferred software development language
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 Cornerstone 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 $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.
Cornerstone Defense AI Hiring
Cornerstone Defense has 1 open AI role 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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