Interested in this AI/ML Engineer role at Cornerstone Defense?
Apply Now →Skills & Technologies
About This Role
Title: SETA AI Engineer
Location: Columbia, MD
Compensation Range: $220 \-$250K
\*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.*
Benefits Overview:
Cornerstone Defense offers a very comprehensive benefits package including, but not limited to: Medical, Dental and Vision Plans \* Generous PTO Policy \* 401(k) \* HSA and FSA options \* Life and Disability Insurance \* Tuition Reimbursement and Training \* Perks at Work Discount Program \* Referral Program \* Leads Generation Program \* CollegeAmerica 529 \* Fitness Reimbursement Program \* Travel Assistance \* Norton Lifelock Benefit Solutions \* Life Planning Financial \& Legal Services \*
Position Description:
Seeking an AI Engineer to provide technical advisory support for the design, evaluation, and advancement of AI\-enabled applications, tools, and workflows. This role is intended for a hands\-on technical professional who understands how modern AI systems are built and can help guide teams developing practical solutions using large language models, agentic workflows, orchestration frameworks, and supporting software infrastructure.
Core Tasks:
- Provide SETA technical advisory support to government teams designing, developing, evaluating, and integrating AI\-enabled applications, tools, and workflows.
- Assess and advise on solutions involving large language models, retrieval\-augmented generation, prompt pipelines, agentic systems, orchestration layers, and evaluation harnesses to ensure mission suitability.
- Review architectures, development approaches, model usage patterns, and technical risks; deliver engineering recommendations, prototypes, guidance, and technical briefs for government stakeholders.
- Evaluate modern AI frameworks, agentic libraries, experimentation harnesses, vector databases, embeddings, inference pipelines, and cloud\-based AI environments for suitability in mission applications
- Support test and evaluation activities including performance analysis, qualitative assessments, workflow validation, and guardrail reviews.
- Advise on AI engineering considerations such as latency, reliability, observability, testability, secure development, data flow, system integration, and operational constraints in government environments.
- Collaborate with platform engineers, mission users, product leads, and leadership to ensure AI solutions are technically grounded, mission\-aligned, and operationally relevant.
- Translate complex technical concepts into clear communication for government audiences, including tradeoff analysis, risk identification, emerging trends, and engineering implications.
- Maintain up\-to\-date knowledge of the rapidly evolving AI ecosystem; provide continuous recommendations on emerging tools, practices, frameworks, and development patterns.
- Support development of knowledge repositories, engineering documentation, evaluation artifacts, and AI assurance products.
Qualifications
- Minimum 18 years of experience with a Bachelor’s degree, or 15 years with a Master’s in Computer Science, Engineering, Data Science, Mathematics, or a related discipline.
- Experience designing, building, integrating, or evaluating AI/ML\-enabled applications or software tools.
- Familiarity with LLM\-based applications, RAG, prompt engineering, agentic workflows, orchestration frameworks, and modern AI application patterns.
- Working knowledge of frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, Haystack, LlamaIndex, or similar ecosystems.
- Experience with software development fundamentals including APIs, data handling, and system integration.
- Ability to assess technical tradeoffs and communicate them effectively to technical and government stakeholders.
- Strong written and verbal communication skills.
Preferred:
- Experience with Python, JavaScript/TypeScript, or similar languages used in AI development.
- Familiarity with vector databases, embeddings, model APIs, evaluation frameworks, and agent testing harnesses.
- Exposure to model hosting, inference pipelines, or cloud\-based AI development environments.
- Understanding of secure AI development practices and data protection requirements in government settings.
- Experience supporting defense, cyber, intelligence, or mission\-focused development.
- Familiarity with human\-in\-the\-loop workflows, AI assurance, and evaluation methodologies.
Salary Context
This $220K-$250K range is above the 75th percentile 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 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 $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 ($235K) sits 9% above the category median. Disclosed range: $220K to $250K.
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.
Cornerstone Defense AI Hiring
Cornerstone Defense has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbia, SC, US. Compensation range: $250K - $250K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.