Interested in this AI/ML Engineer role at General Dynamics Information Technology?
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About This Role
Clearance Level
None
Category
Data Science and Data Engineering
Location
Springfield, Virginia
*(Onsite Workplace)*
Key Skills For Success
Algorithms
Artificial Intelligence (AI)
Machine Learning (ML)
Machine Learning Algorithms
##### REQ\#:RQ223607
##### Public Trust:None
##### Requisition Type:Regular
##### Your Impact
Own your opportunity to serve as a critical component of our nation’s safety and security. Make an impact by using your expertise to protect our country from threats.
Job Description
-------------------
AI/ML ENGINEER SENIOR
At GDIT, our people are our business. We pride ourselves on the work our innovators do to support and secure some of the most complex government, defense, and intelligence projects across the world.
We are passionate about leveraging artificial intelligence and machine learning to solve real\-world problems and deliver innovative solutions. Your work in enterprise IT will help our clients overcome evolving demands and missions. Own the opportunity to build your skills and accelerate digital modernization for our clients as you accelerate your career. We wouldn't be successful without our ability to attract new talent to join our team.
Job Description Summary:
Our AI/ML Engineer will use statistical techniques to provide insights into internal or external customer data to guide decision\-making and enhance user experience. This will include building machine learning algorithms or work on artificial intelligence system deployments. You will work alongside experienced engineers and data scientists to develop and implement machine learning models and AI solutions. The ideal candidate will have knowledge of computer languages and their applications to enable a system to perform specific functions.
In this position you will Explore and experiment with new algorithms, techniques, and tools to improve model performance and solution quality You will also closely with senior engineers and data scientists to integrate machine learning models into production systems.
MEANINGFUL WORK AND PERSONAL IMPACT
- Model development by supporting the development, training, and optimization of machine learning models for various applications.
- Work with large datasets to clean, preprocess, and transform data into formats suitable for machine learning models.
- Evaluating the performance of machine learning models using the applicable metrics and techniques
GDIT JOB DESCRIPTION:
- Completion of engineering tasks
- Development of solutions to engineering problems of limited scope
- Systems design, development, and integration from the start of a systems life cycle to the end
- Oversight of infrastructure including installing, configuring, testing, and maintaining operating systems, application software, and system management tools
- Perform systems modeling, simulation, and analysis
- Continual maintenance of security, including backup and redundancy strategies
- Documentation for processes and procedures related to engineering projects
- Maintains current knowledge of rapidly changing technology
WHAT YOU’LL NEED TO SUCCEED
Bring your expertise and drive for innovation to GDIT. The AI/ML Engineer Senior must have:
- Education: Bachelor of Arts/Bachelor of Science
- Experience: 5\+ years of related experience
- Technical skills:
- Knowledge of Python or other programming languages
- Problem solving and an understanding of big data.
- An understanding and interest in data transformation, model selection, cross\-validation, and deployment
- Interest and experience with big data platform tools
- Knowledge of statistical techniques
- Experience working in a collaborative team environment.
- Intuitiveness with an ability to identify and solve complex problems related to rapidly changing technology.
- Ability to effectively articulate technical challenges and solutions.
- Designs and defines system architecture for new or existing computer systems.
- Coordinates system development to include design, modeling, security, integration, and formal testing
- Security clearance level: Must be able to obtain TS/SCI w/Poly
- US citizenship required
GDIT IS YOUR PLACE
At GDIT, the mission is our purpose, and our people are at the center of everything we do.
- Growth: AI\-powered career tool that identifies career steps and learning opportunities
- Support: An internal mobility team focused on helping you achieve your career goals
- Rewards: Comprehensive benefits and wellness packages, 401K with company match, and competitive pay and paid time off
- Community: Award\-winning culture of innovation and a military\-friendly workplace
OWN YOUR OPPORTUNITY
Explore a career in data science and engineering at GDIT and you’ll find endless opportunities to grow alongside colleagues who share your determination for solving complex data challenges.
### Work Requirements
Years of Experience
5 \+ years of related experience
- may vary based on technical training, certification(s), *or* degree
Certification
Travel Required
Less than 10%
Citizenship
U.S. Citizenship Required
### Salary and Benefit Information
The likely salary range for this position is $161,500 \- $218,500\. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.
### Our Identity Verification Process
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.
### About Our Work
We are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50\+ countries worldwide, offering leading mission\-ready capabilities in AI, cloud, cyber and software development.
Join our Talent Community to stay up to date on our career opportunities and events at gdit.com/tc.
*Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans*
Salary Context
This $161K-$218K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At General Dynamics Information Technology, 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. This role's midpoint ($190K) sits 13% below the category median. Disclosed range: $161K to $218K.
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.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span Bethesda, MD, US, Springfield, VA, US, Fort Bragg, NC, US. Compensation range: $164K - $304K.
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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