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About This Role
DESE Research, Inc. is seeking a talented, motivated, and enthusiastic AI Security and Safety Assurance Engineer to join a multidisciplinary team of analysts, researchers, developers, and engineers. This role focuses on constructing and evaluating risk analysis and assurance methodologies as they apply to Artificial Intelligence (AI) and Machine Learning (ML) within mission\-critical defense systems. If you are passionate about responsible AI, risk analysis, and enhancing the resilience of military systems, we want to hear from you!
Job Details:
The candidate will work as part of a small team in conducting research, developing and executing assurance methodologies, and identifying areas of risk, threats, and vulnerabilities associated with AI systems, pipelines, and components. To be successful in this role, the candidate will need knowledge and understanding of Army acquisition, emerging DoW and Army guidance; AI/ML security and safety; and assurance methodologies. A successful candidate will possess strong analytical skills, communicate effectively with technical teams and customers, and thrive in a collaborative engineering environment. This role is on\-site in Huntsville, AL.
Responsibilities:
- Conduct research and analysis on Responsible/Ethical AI concepts, initiatives, and evaluation methods.
- Identify AI\-enabled system functionality and critical components by analyzing and decomposing complex systems from documentation as well as input from engineering subject matter experts.
- Develop and apply risk analysis and assessment methodologies for AI/ML in mission\-critical systems.
- Collaborate with Acquisition, AI/ML, Software Engineering, and Systems Engineering teams to ensure survivability and resiliency of warfighting systems.
- Produce high\-quality technical writing and oral presentations on research findings, incorporating data from disparate sources or organizations into a cohesive narrative.
- Build meaningful relationships with researchers and stakeholders across organizations.
- Stay informed on advancements in AI risk analysis, governance, and DoW initiatives.
Required Qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, Systems Engineering, Software Engineering, Data Science, Information Systems, Cybersecurity, or a related technical discipline.
- 5 years of experience in security assurance, risk management, AI/ML governance, or related field.
- Active Secret security clearance or the ability to obtain and maintain one.
Preferred Qualifications:
- Demonstrated excellence in independent research across academia, industry, or government.
- Proficiency in technical writing and effectively communicating research results.
- Strong critical thinking and analytic skills.
- Self\-motivated, proactive, and adaptable to changing project needs.
- Ability to foster collaboration within a multidisciplinary team while supporting a customer\-facing engineering environment.
- Understanding of AI development lifecycle processes and MLSecOps best practices.
- Experience in AI threat modeling, AI risk research, or related disciplines.
- Experience training and using Machine Learning models.
- Experience using AI observability, tracing, and evaluation tools (e.g., MLflow, Langfuse).
- Experience with adversarial testing methods and AI red\-teaming tools.
- Familiarity with ML frameworks (e.g., TensorFlow, PyTorch) and AI orchestration frameworks (e.g., LangChain, LlamaIndex).
- Experience with the DoW AI Assurance Toolkit or other AI policy and guidance initiatives.
- Awareness of evolving AI regulations, standards, and best practices.
- Familiarity with Army acquisition lifecycles and System Security Engineering.
- Familiarity with AI governance frameworks, standards, and controls (e.g., NIST AI Risk Management Framework (AI RMF), ISO/IEC 42001, NIST COSAiS Project).
- Familiarity with ATLAS (Adversarial Threat Landscape for Artificial\-Intelligence Systems) or similar frameworks.
- Knowledge of weapon systems, embedded system platforms, and AI\-enabled defense systems.
The ideal candidate is a career\-minded individual who wants to become a part of something bigger. DESE has industry\-leading benefits including a generous profit\-sharing plan, competitive pay, but perhaps most importantly, the opportunity to work with the best and the brightest in the industry in support of the US warfighter. Launching a career with DESE allows you to put your skills and experience to good use doing crucial, mission\-critical work that our country's armed forces and others depend on.
DESE is committed to creating a company that is known for its respect and care for employees. We understand that happy employees are what keeps our business going and we strive to provide the best opportunities for each individual working on our team! Here are a few reasons you will love working for DESE:
- Competitive salaries
- Annual performance bonuses
- Robust 401K profit sharing plan
- Competitive health, dental \& vision insurance with affordable premiums
- Two different flexible spending account options
- Company paid life insurance \& Accidental Death \& Dismemberment
- Education reimbursement program
- Personal leave for approved philanthropic activities
- Vacation, Sick, \& Holiday leave
- Opportunities for internal promotions
- Employee referral incentive program
- Rewards and gifts for service anniversaries
Disability Accommodation for Applicants \- DESE Research, Inc. is an Equal Employment Opportunity employer and provides reasonable accommodation for qualified individuals with disabilities and disabled veterans in its job application procedures. If you have any difficulty using our online system and you need an accommodation due to a disability, you may use the following alternative email address or phone number to contact us about your interest in employment with us: [email protected] or 256\-837\-8004x123\.
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 DESE Research, Inc., 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.
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.
DESE Research, Inc. AI Hiring
DESE Research, Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Huntsville, AL, US.
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
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