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About This Role
Pipeline Requisition – Proposal (Anticipated Start: September 2026\)
Location: Wright Patterson Air Force Base, Dayton, OH (subject to award)
Contract Type: Proposal Pipeline – Positions contingent upon contract award
Overview: We are building a talent pipeline in support of an upcoming contract proposal to provide advisory and assistance services to Air Force Life Cycle Management Center (AFLCMC) to support the National Air and Space Intelligence Center (NASIC) mission areas including engineering, analysis, information technology and cyber security, with anticipated work beginning in September 2026. This position will support NASIC’s production of technical intelligence; modernize capabilities; support intelligence community collaboration and Department of War (DoW) mission needs.
Work will be performed on\-site at Wright Patterson Air Force Base, Dayton, OH, pending award.
Required Qualifications:
- Ability to obtain and maintain a DoD security clearance (Secret or TS/SCI depending on role)
- Strong background directly aligned with the technical or analytical specialty area
- Strong analytical, communication, and writing skills
- For some roles, advanced degrees (MS/PhD) are preferred
Desired Qualifications
- Experience supporting DoW, interagency, or national security missions is highly desirable
- Experience within the National Air and Space Intelligence Center (NASIC) community.
*We are currently gathering interest and pre\-qualifying candidates for the potential role described below:*
Artificial Intelligence/Machine Learning Engineer
The Artificial Intelligence/Machine Learning (AI/ML) Engineer provides engineering support to deliver secure, low‑risk technical solutions and effective program execution, including the integration of intelligent automation, AI, and machine learning. Responsibilities include developing and managing programs to guide digital transformation to cloud computing environments and the implementation of intelligent automation, AI and ML capabilities. The engineer leads programs that guide the application of hardware, software, and policy to modernize and adapt legacy systems for hybrid classified cloud environments. Additionally, the engineer develops and oversees programs that enable the adoption and operationalization of IA/AI/ML solutions, ensuring alignment with mission priorities, data strategies, and security requirements.
Desired Experience:
- Bachelor’s degree in computer science/engineering or related discipline
- Minimum 5 years of related experience.
- Requires proficiency in complete software development lifecycle.
- 3\+ years of experience in AL/ML\-based solutions, including Python programming and deep learning frameworks such as PyTorch, TensorFlow, or Keras
- Experience developing Natural Language Processing (NLP), Large Language Models (LLM), and Retrieval\-Augmented Generation (RAG)\-enabled solutions
- Experience applying AI/ML research to develop AI/ML solutions
- Knowledge of core AI/ML concepts, including clustering, regression, classification, algorithm selection, and model evaluation
- Requires good oral and written communication skills, interpersonal skills, including ability to adapt and work effectively in a variety of configurations independently, as a member of a team, and in a matrix reporting environment.
- Requires ability to prioritize tasks, show considerable initiative, resolve conflicts and handle complaints.
About Us – Design Interactive, Inc.
Founded in 1998, Design Interactive (DI) is a small business dedicated to optimizing and enhancing human performance by utilizing cutting\-edge technology, pioneering research, and focusing on digital transformation.
We deliver end\-to\-end solutions—Huan Factors \& Human Systems Integration, UX/UI Design \& Human\-Centered Design, Digital Transformation, Applied Research \& Technology Development, Data Science \& Advanced Analytics, Artificial Intelligence \& Machine Learning (AI/ML), Life Cycle Software Development, and System \& Software Integration. DI solves high impact performance challenges across both defense and commercial industries.
Our Mission:
We Empower People with Innovative Technology.
Our Culture:
We’re committed to an inclusive and diverse workplace where every individual feels valued and respected. We provide equal opportunities to all candidates, regardless of background or identity.
Equal Employment Opportunity (EEO) Statement
We encourage applications from candidates of all backgrounds, experiences, and abilities—including those from underrepresented communities in technology.
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 Design Interactive 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Design Interactive Inc AI Hiring
Design Interactive Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dayton, OH, 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
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