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About This Role
Overview: About Us
Working across the globe, V2X builds smart solutions designed to integrate physical and digital infrastructure from base to battlefield. We bring 120 years of successful mission support to improve security, streamline logistics, and enhance readiness. Aligned around a shared purpose, our $4.5B company and 16,000 people work alongside our clients, here and abroad, to tackle their most complex challenges with integrity, respect, responsibility, and professionalism. Responsibilities: What You'll Do
- The AI-Ready Endpoint & MDM Engineer is a technical role responsible for the evaluation, operation, maintenance, security, and evolution of the global endpoint environment. Reporting to the Director of AI-Ready Endpoint Infrastructure & Security, this engineer will utilize agentic AI and modern configuration management to automate the administration of the company’s unified endpoint ecosystem. This role provides engineering and development across multiple Mobile Device Management (MDM) platforms (Intune, Jamf, Google MDM, Automox) and cloud workspace solutions, including Microsoft Azure Virtual Desktop (AVD) managed via Nerdio. The engineer ensures all endpoints are hardened, compliant, and continuously prepared to serve as secure platforms for both human users and AI workloads, while applying FinOps principles to optimize cloud performance and financial efficiency.
Key Responsibilities* Azure Virtual Desktop (AVD) & Cloud Workspace Management
- + Architect, deploy, and manage Microsoft Azure Virtual Desktop (AVD) environments purpose-built to support multiple user personas, tailoring compute, storage, and application delivery to distinct organizational roles and workload requirements. + Utilize Nerdio Manager for Enterprise to centrally manage AVD deployments, automate image staging, and streamline the provisioning of Windows cloud desktops and remote applications. + Maintain operating efficiency and stability for remote desktop services and Azure VDI Desktops, ensuring secure, high-performance remote access.
- FinOps & Infrastructure Cost Optimization
- + Implement FinOps best practices by leveraging Nerdio's advanced auto-scaling capabilities to dynamically adjust active session host VMs based on real-time user demand, significantly reducing monthly Azure compute and storage costs. + Balance infrastructure cost optimization with a seamless user experience by configuring hybrid auto-scaling strategies that combine proactive schedule-based scaling (pre-warming capacity) with reactive performance-based scaling (CPU/memory triggers). + Optimize storage expenditures by automating OS disk downgrades for non-persistent machines during idle periods and utilizing "Start on Connect" logic for personal 1:1 assigned desktops.
- Unified Endpoint & MDM Engineering
- + Develop, deploy, and maintain management software and systems across a diverse global fleet of endpoints, including Windows 11 PCs, MacBooks, Chromebooks, Windows Servers, RHEL servers, Android, and iOS devices. + Configure and optimize enterprise MDM platforms (Microsoft Intune, Jamf, Google MDM, and Automox) to enforce configuration baselines and compliance policies. + Manage Operating System Deployment (OSD) task sequences, OS images, and automated software packaging pipelines for complex, multi-platform environments.
- Agentic AI, Automation & Patch Management
- + Leverage agentic AI workflows to automate device lifecycle management, from zero-touch provisioning and onboarding to offboarding and secure decommission. + Integrate intelligent agents to monitor endpoint health, interpret compliance data, and execute self-healing remediations autonomously across the MDM infrastructure. + Manage the testing and deployment of third-party patching, as well as workstation and server security, feature, and quality updates across all operating systems.
- Governance & Support
- + Gather information and implement policies required for legal and regulatory compliance, such as CMMC Level 2 and NIST 800-171 controls. + Prioritize and respond to requests for service, providing Tier 3 escalation support and guidance to global endpoint support staff in remote locations.
Qualifications: Minimum Qualifications* Education: + Bachelor’s Degree in IT, Computer Science, or a related field or an equivalent combination of education and experience from which comparable knowledge and job skills can be obtained. (One year related experience may be substituted for one year of education, if degree is required)
- Experience: + Minimum of 4 to 6 years of related IT experience, with at least 3 years focused specifically on endpoint systems administration, MDM platform management, and cloud virtualization.
- Other Requirements: + U.S. Citizenship required.
- Required Skills & Competencies: + Hands-on administrative expertise across multiple UEM/MDM platforms, specifically Microsoft Intune, Jamf, Google MDM, and Automox. + Deep proficiency in cross-platform operating system management, including Windows 11, macOS, ChromeOS, Windows Server, RHEL, iOS, and Android. + Expertise in designing and managing Azure Virtual Desktop (AVD) environments optimized for multiple personas and workloads. + Strong understanding of cloud FinOps principles and hands-on experience using Nerdio Manager for Enterprise for automated scaling, cost optimization, and dynamic resource provisioning. + Familiarity with extending configuration management and Zero Trust device posture checks to monitor and govern AI agents running on endpoints. + Proficiency in scripting languages (PowerShell, Bash, Python) and utilizing APIs to integrate platforms.
What We Bring
- At V2X we strive to be market competitive in our total reward offerings.
- The successful candidate’s starting pay will be based on, but not limited to, their job related skills, experience, qualifications, work location, and market conditions.
- The following salary range is intended to display the value of the company’s base pay compensation and may be modified at the discretion of the company. + USD $ 130,000 - 190,000 + Provided salary range minimum and maximum values correspond to variances between regional/geographic locations across the United States.
- Please speak with a recruiter for additional information.
- Employee benefits include the following: + Healthcare coverage + Retirement plan + Life insurance, AD&D, and disability benefits + Wellness programs + Paid time off, including holidays + Learning and Development resources + Employee assistance resources
- Pay and benefits are subject to change at any time and may be modified at the discretion of the company, consistent with the terms of any applicable compensation or benefit plans.
At V2X, we are deeply committed to both equal employment opportunity, including protection for Veterans and individuals with disabilities, and fostering an inclusive and diverse workplace. We ensure all individuals are treated with fairness, respect, and dignity, recognizing the strength that comes from a workforce rich in diverse experiences, perspectives, and skills. This commitment, aligned with our core Vision and Values of Integrity, Respect, and Responsibility, allows us to leverage differences, encourage innovation, and expand our success in the global marketplace, ultimately enabling us to best serve our clients.
Salary Context
This $130K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At V2X, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $130K to $190K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
V2X AI Hiring
V2X has 5 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $235K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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