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About This Role
Clearance Level Top Secret/SCI Category Data Science and Data Engineering Location Remote, Working from the USA
Key Skills For Success Artificial Intelligence Technologies
Design
Sensor Fusion
Sensors
Unmanned System
REQ#:RQ230185
Public Trust:None
Requisition Type:Regular
Your Impact
Own your opportunity to support our nation's defense. Make an impact by connecting and securing critical operations across the globe, keeping our country safe and secure.
Job Description
PRINCIPAL AI AUTONOMY SYSTEMS INTEGRATOR (UAS / SWARMING / C2 / EDGE AI) Hands‑On Engineering • Autonomous System Integration • Mission‑Thread Enablement
Itegrate autonomy software with real assets, validate behaviors in simulation and field environments, and ensure that autonomous agents act over trusted data, in real time, under real mission pressures.
MISSION & IMPACTAutonomy Integration & System Engineering
- Integrate autonomy stacks onto UAS, multi‑vehicle swarms, and robotic platforms, including navigation, perception, cooperative behaviors, and distributed decision-making.
- Implement real‑time autonomy pipelines that incorporate sensing, fusion, planning, and control across diverse air/ground systems.
- Adapt and optimize autonomy algorithms for constrained edge environments (embedded systems, GPUs/VPUs, tactical compute nodes).
Data‑ and Knowledge‑Aligned Autonomy
- Build data pipelines and semantic alignment layers so autonomous systems operate against trusted, structured, and meaning‑preserving data representations.
- Integrate autonomy with ontology-driven or schema-governed data models to ensure consistent entity tracking, mission-state representation, and decision context.
- Enable retrieval‑grounded behaviors where autonomous agents act based on authoritative, updated, and fused situational information.
Edge Deployment Across Contested Environments
- Deploy AI/ML models and autonomy services to edge compute environments (K3s/RKE2, containers, bare metal).
- Harden autonomy behaviors for degraded communications, GPS denial, EW threats, sensor loss, and variable latency.
- Implement resilient synchronization patterns between edge devices and enterprise/tactical nodes.
Simulation, Experimentation, and Field Testing
- Conduct structured field experiments with unmanned vehicles, swarms, multi‑agent behaviors, and C2 workflows.
- Validate mission‑thread performance across detection, tasking, coordination, and operator-in-the-loop behaviors.
Agentic Workflow Integration
- Integrate agent frameworks enabling autonomy agents to plan, coordinate, task, and interact with mission tools and human operators.
- Define safety gates, oversight mechanisms, and deterministic tool‑use patterns that support “human‑on‑the‑loop” operations.
- Support multi‑agent cooperation through standard APIs and interoperable messaging patterns.
C2, Networks, and Cross‑Domain Integration
- Integrate autonomous systems with tactical C2 platforms, data links, secure communications, and multi‑domain operational workflows.
- Implement open standards (MOSA, SOSA, CMOSS, open messaging protocols) for plug‑and‑play component integration.
- Enable interoperability across joint, partner‑nation, and coalition environments with appropriate policy controls.
WHAT YOU WILL DO
- Build full autonomy pipelines end‑to‑end, from sensing through action execution.
- Deploy CV, tracking, SLAM, pattern‑learning, anomaly detection, and cooperative‑agent models on real unmanned systems.
- Integrate heterogeneous sensors, radios, and payloads with autonomy decision loops.
- Conduct troubleshooting and rapid problem resolution across autonomy software, embedded systems, edge clusters, and C2 interfaces.
- Translate mission problems into executable autonomy behaviors and scenario‑specific workflows.
- Train teams and mission personnel on system configuration, safety, deployment, and best practices.
REQUIRED EXPERIENCECore Background
- Education: BA/BS or equivalent
- Experience: 10+ years in unmanned systems, autonomy engineering, robotics integration, or embedded AI/ML deployment.
- Direct experience integrating autonomy into UAS, drones, swarms, robotic vehicles, or similar platforms.
- Strong proficiency in C++, Python, and real‑time/embedded software patterns.
Technical Expertise
- Autonomy functions: motion planning, cooperative behaviors, obstacle avoidance, distributed coordination, sensor fusion, and real‑time decision-making.
- Robotics frameworks: ROS/ROS2, PX4/ArduPilot, MAVLink, simulation tools (Gazebo, AirSim, custom environments).
- AI/ML frameworks: PyTorch, TensorFlow, ONNX, JAX.
- Edge compute: NVIDIA Jetson, VPUs, ruggedized x86 nodes, tactical accelerators.
- Containerization and DevSecOps: Docker, Kubernetes (K3s/RKE2), GitLab CI, ArgoCD.
- Interoperability standards: open architectures, modular interfaces, secure API design.
Domain Understanding
- Experience supporting C2 integration, tactical networks, and degraded-communication workflows.
- Understanding of multi‑domain mission threads such as ISR, counter‑UAS, logistics autonomy, multi-agent coordination, and decision-support.
- Experience designing resilient behavior under environmental, adversarial, and operational stressors.
Security Requirements
- U.S. Citizenship required.
- Top Secret/SCI Required to start
DESIRED EXPERIENCE
- Experience with distributed track fusion, detection/classification pipelines, or multi-sensor correlation.
- Familiarity with mission‑partner interoperability and cross‑domain data release patterns.
- Experience implementing provenance, auditability, and safety‑bounded autonomy behaviors.
- Background in private 5G, tactical mesh networking, or mobile operations centers.
WHY THIS ROLE IS UNIQUE This Principal-level role is tailored for someone who:
- Builds autonomy that works in real field conditions, not just in demos.
- Bridges AI models, mission data, and real unmanned systems.
- Ensures autonomous behaviors are grounded in trusted, structured information.
- Understands both the engineering and the operational realities of deploying autonomy at scale.
- Can integrate across sensors, payloads, compute, C2, and enterprise systems.
You are the person who makes autonomous systems reliable, interoperable, safe, and mission-ready.
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 at GDIT and you’ll find endless opportunities to grow alongside colleagues who share your sense of ownership and pride in the meaningful work we do.
Work Requirements
Years of Experience 10 + years of related experience
- may vary based on technical training, certification(s), or degree
Certification Travel Required 25-50%
Citizenship U.S. Citizenship Required
Salary and Benefit Information
The likely salary range for this position is $149,469 - $201,250. 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 $149K-$201K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $149K to $201K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Architect, Data Scientist. Positions span Falls Church, VA, US, Sterling, VA, Remote, US. Compensation range: $98K - $253K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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