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About This Role
Davidson is distinguished in the aerospace and missile defense industry, in part, because of its excellence in hiring exceptional experts, engineers and software developers dedicated to delivering advanced, intelligent technology solutions in defense of our nation.
Davidson is seeking an AI (ML) Engineer in Colorado Springs, CO.
This position is an integral part of a fast-paced, innovative program for the Missile Defense Agency (MDA) involving the creation of advanced M&S, Optimization, and AI capabilities in support of the Guam Defense System (GDS). The program works cross-functionally with innovative team members skilled in Modeling & Simulation (M&S), Artificial Intelligence (AI)/Machine Learning (ML), DevSecOps, and Cybersecurity.
The ideal candidate will possess a fundamental understanding of AI/ML principles, experience supporting AI/ML projects from conception to deployment, and strong communication skills. As an AI/ML Engineer, you will be responsible for developing AI/ML products directly feeding the AI/ML strategy, communicating AI/ML concepts and results to both technical and non-technical audiences, and collaborating with cross-functional teams to deliver innovative AI-powered solutions. The selected individual must be able to work with little direct supervision, work as an integral member of a product delivery team and meet time critical delivery schedules.
Work will be performed at a contractor facility in Colorado Springs, CO and at Schriever SFB.
All team members should be able to:
- Experiment with innovative theories, ideas, and concepts to expand the realm of the possible
- Self-organize and participate in an Agile development lifecycle
- Ask questions and build consensus within a cross-functional team
- Provide design inputs and collaborate to decompose, estimate, and refine backlog requirements
Job Responsibilities:
- Candidate will serve as an AI Engineer for a team creating multiple AI/ML projects and the development and training environment in which they are created.
- Primary responsibilities include close collaboration with data team members, collaboration with functional SMEs, and developing advanced optimization models for customer product enhancement
- Train models and algorithms within a software product across a cross-functional team to provide value to our customer
- Support the full lifecycle of AI/ML projects, from problem definition and data collection to model development, deployment, and monitoring.
- Design, develop, and implement AI/ML models and algorithms using appropriate tools and techniques.
- Ensure the quality and accuracy of AI/ML models through rigorous testing and validation.
- Collaborate with software engineers, network security, and DevOps teams to deploy AI/ML models into production environments.
- Model performance of AI/ML models in production and identify areas for improvement.
- Communicate AI/ML concepts and results effectively to both technical and non-technical audiences.
- Work in close coordination with multiple teams that will use AI/ML products.
- Work with AI/ML Lead to define problem statements and success metrics for AI/ML products.
Job Requirements:
- Bachelor of Science degree in a STEM (Science, Technology, Engineering, or Math) discipline with 4 to 6+ years of relevant experience, or a Master's degree and 3 years experience.
- AI Experience + Familiarity in a variety of AI/ML techniques, including deep learning, natural language processing, computer vision, and reinforcement learning. + Experience with Large Language Models/Generative AI and Prompt Engineering. Also, using LLM/GAI solutions for usage in human-machine teaming for Software Development. Algorithmic understanding of heuristic and black box approaches for optimization, such as Genetic Algorithms, Simulated Annealing, Bayesian Optimization, and Particle Swarm Optimization. + Familiarity with ML algorithms, such as Decision Tree, Random Forest, Support Vector Machine, Neural Network, Convolutional Neural Network, Long Short-Term Memory. + Experience evaluating models using common AI/ML concepts (e.g. cross-validation, drift monitoring, model cards, bias/fairness testing). + Familiarity with response AI/model governance.
- AI Development Experience + Proficient in building optimization models with one of the following languages: Python, C, C++ + Understanding of Python's common data stack (NumPy, Pandas, SciPy) + Experience with common ML frameworks for Python (scikit-learn, Tensorflow, PyTorch)
- Development Environment Experience + Experience with collaborative programming tools (Git, Gitlab) + Familiarity with Linux operating systems.
- Team Environment and Communication + Strong communication and interpersonal skills; including presentation skills. + Strong problem-solving and analytical skills. + Must be self-motivated, able to work effectively in a dynamic and collaborative team environment.
- Ability for some limited travel (less than 10% time) to support program meetings.
- Excellent communication skills
Desired Competencies/Experience/Certifications:
- 5+ years of relevant experience in Operations Research/Machine Learning
- Demonstrable experience building ML models in Python
- Experience with data manipulation and ML for big datasets
- Experience with MLOps tools (Kubeflow, MLflow, DVC)
- Knowledge of modern application development to include containerization (Docker/K8S)
- Demonstrable experience applying analytical and programming skills to solve optimization problems
- Strong algorithmic understanding of classical optimization techniques. (e.g., linear programming, gradient descent).
- Familiarity with Agile processes/development methodology
- Familiarity with cloud computing platforms such as AWS, Azure, or GCP.
Clearance Requirements:
- Holds active DoD Secret clearance at time of application and able to obtain Top Secret Security clearance.
Salary Context
This $119K-$142K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Davidson Technologies, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($130K) sits 38% below the category median. Disclosed range: $119K to $142K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Davidson Technologies, Inc. AI Hiring
Davidson Technologies, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Colorado Springs, CO, US. Compensation range: $142K - $142K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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