Interested in this AI/ML Engineer role at General Dynamics Mission Systems?
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About This Role
Basic Qualifications :
Bachelor's degree in Software Engineering, or related Science, Technology, Engineering or Mathematics field, plus a minimum of 10 years of relevant experience; or Master's degree, plus 8 years relevant experience. CLEARANCE REQUIREMENTS: Ability to obtain a Department of Defense Secret security clearance is required at time of hire. Applicants selected will be subject to a U.S. Government security investigation and must meet eligibility requirements for access to classified information. Due to the nature of work performed within our facilities, U.S. citizenship is required. Responsibilities for this Position: The Opportunity
General Dynamics Mission Systems operates AI-enabled capabilities across multiple platforms, providers, applications, and security environments. As human and agentic usage expands, leaders and operators need timely, trusted visibility into consumption, capacity, performance, adoption, value, and financial investment.
The AI Analytics Technical Lead is a hands-on senior individual contributor who will connect governed source data, enterprise data platforms, semantic standards, and owner-approved metrics into consistent and decision-ready analytical products. The role leads technical delivery through a matrixed team without direct-report responsibility.
The role owns AI analytics integration, data contracts, shared transformations, quality controls, reporting-pipeline design, and analytical products assigned to Innovation & Engineering. It partners with, but does not replace, the teams that own Snowflake, the enterprise data warehouse, the enterprise ontology, source-application production, source systems, business metric definitions, and financial policy.
What You Will Do
- Translate AI investment and operational decision needs into phased requirements, integration architecture, interfaces, acceptance criteria, and a technical roadmap.
- Connect approved telemetry, usage, capacity, cloud billing, application, agent, identity, organization, program, and financial-reference data through governed interfaces to enterprise data platforms.
- Co-design and implement versioned data and telemetry contracts that preserve identity, application, project, program, requested and served model, provider, environment, consumption, and traceable investment inputs.
- Build analytics-ready fact models, transformations, lineage, quality controls, reconciliation, replay, recovery, and historical reproducibility on owner-approved enterprise platforms.
- Establish one governed reporting path across approved dashboards, reports, emails, extracts, alerts, and interfaces, with consistent results and visible data status.
- Implement and publish metric definitions approved by value-stream and business owners without creating conflicting local definitions or duplicate systems of record.
- Produce technical readiness evidence, including service objectives, monitoring, reconciliation, recovery tests, and rollback criteria, while source and platform owners retain production authority.
- Lead engineers, source owners, enterprise data teams, ontology owners, platform teams, security, privacy, Finance, and metric owners around a shared roadmap and evidence-based decisions.
First-Year Outcomes
- Baseline priority sources, reports, owners, definitions, interfaces, data quality, attribution coverage, reporting timeliness, and manual effort.
- Publish an approved integration roadmap, report catalog, interface requirements, acceptance criteria, and ownership map.
- Put priority integrations and reports into repeatable operation on approved enterprise platforms with automated quality, reconciliation, monitoring, and recovery evidence.
- Deliver a governed interface from an initial AI-enabled source application and reusable patterns for additional sources, with consistent operational and executive reporting across approved surfaces.
What We Are Looking For
You are a senior software, data-integration, and analytics technical leader who remains hands-on. You can build critical transformations and reporting components, lead across organizational boundaries, and explain evidence and tradeoffs to senior leaders. You know how to create a trusted path from source data to decisions while respecting platform, source, ontology, metric, privacy, and policy ownership.
Required
- Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems, Statistics, or a related technical discipline with at least 10 years of relevant experience, or a master's degree with at least 8 years of relevant experience. An equivalent combination may be considered in accordance with GDMS requirements.
- Demonstrated experience as a technical lead or principal engineer for a production analytics, telemetry, data-integration, or Financial Operations capability, including matrixed leadership.
- Strong SQL and Python skills, with hands-on experience building governed transformations, analytical fact models, data-quality controls, and reporting pipelines on enterprise data platforms.
- Experience with versioned data contracts, application programming interfaces, stable identifiers, idempotent processing, replay, reconciliation, schema evolution, observability, recovery, parallel validation, and controlled cutover.
- Experience integrating cloud billing, platform telemetry, model gateways, identity, organizational data, program metadata, and financial-reference data through governed interfaces.
- Experience with semantic and ontology mappings, effective-dated dimensions, lineage, metric versioning, and reproducible reporting.
- Experience applying privacy controls to individual-level data, including row-level access, de-identification, retention, aggregation thresholds, and approved management-reporting boundaries.
- Understanding of AI platform mechanics, including tokens, context, caching, inference, model tiers, routing, rate limits, latency, utilization, and agentic execution.
Preferred
- Experience in defense and aerospace, including controlled unclassified information and classified-environment data handling.
- Experience with AI or machine-learning platform operations, model-serving infrastructure, or large language model deployment at enterprise scale.
- Experience with Snowflake, Azure, Databricks, PostgreSQL, event-driven and lakehouse patterns, Denodo, Power BI, continuous integration and delivery, data observability, and performance testing.
- Experience integrating enterprise ontology, semantic models, identity services, model management, and governed application programming interfaces into analytical products.
- Experience with automated operational and financial reporting, cloud-consumption analytics, agent attribution, anomaly detection, forecasting, or scenario modeling.
Why This Role Matters
AI investment and operational decisions depend on trusted information. This role will provide the technical integration and reporting leadership needed to connect source systems, enterprise platforms, semantic standards, and owner-approved metrics into timely and defensible decision products without creating a competing data platform.
Our Commitment to You:* An exciting career path with opportunities for continuous learning and development.
- Research oriented work, alongside award winning teams developing practical solutions for our nation’s security
- Flexible schedules with every other Friday off work, if desired (9/80 schedule)
- Competitive benefits, including 401k matching, flex time off, paid parental leave, healthcare benefits, health & wellness programs, employee resource and social groups, and more
- See more at gdmissionsystems.com/careers/why-work-for-us/benefits
Workplace Options:
This position is eligible for Remote, Hybrid, or On-Site. Proximity to a major GDMS facility is requyired, as periodic on-site collaboration is expected. Salary Note: This estimate represents the typical salary range for this position based on experience and other factors (geographic location, etc.). Actual pay may vary. This job posting will remain open until the position is filled. Combined Salary Range: USD $183,371.00 - USD $203,428.00 /Yr. Company Overview:
General Dynamics Mission Systems (GDMS) engineers a diverse portfolio of high technology solutions, products and services that enable customers to successfully execute missions across all domains of operation. With a global team of 12,000+ top professionals, we partner with the best in industry to expand the bounds of innovation in the defense and scientific arenas. Given the nature of our work and who we are, we value trust, honesty, alignment and transparency. We offer highly competitive benefits and pride ourselves in being a great place to work with a shared sense of purpose. You will also enjoy a flexible work environment where contributions are recognized and rewarded. If who we are and what we do resonates with you, we invite you to join our high-performance team!
Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans
Salary Context
This $183K-$203K range is above 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 Mission Systems, 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 ($193K) sits 9% below the category median. Disclosed range: $183K to $203K.
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 Mission Systems AI Hiring
General Dynamics Mission Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $203K - $203K.
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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