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About This Role
WWC Global, an operating firm of Command Holdings, is seeking a Machine Learning (ML) Engineer to serve on a potential contract supporting USSOCOM's mission to transform the SOF Enterprise into a decisive data\-centric organization by applying scientific methods, algorithms, processes, and systems to extract actionable insights and knowledge from both structured and unstructured data, enabling the full spectrum of analysis, including descriptive, diagnostic, predictive, and prescriptive analytics.
Responsibilities may include, but are not limited to:
- Designing, developing, deploying, and maintaining machine learning models and AI\-enabled systems that support SOF Enterprise data analysis, operational planning, and decision advantage.
- Developing AI/ML algorithms and models including those designed for Natural Language Processing (NLP), computer vision, Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), and decision\-making that learn from data to identify patterns and improve performance over time.
- Building and managing end\-to\-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring.
- Conducting AI/ML modeling and simulation efforts to create models that analyze data, recognize patterns, and make predictions, and using those models to simulate real\-world scenarios to test various outcomes without real\-world risk.
- Implementing MLOps practices to ensure scalable, repeatable, and auditable model development and deployment workflows.
- Conducting suitability and feasibility assessments to confirm AI/ML is the appropriate tool for a given task and establishing clear requirements and fully specified tasks prior to development.
- Ensuring all LLM/RAG experimentation and model development adheres to mandatory security and data governance requirements defined by Federal, DoW, and SOCOM policies.
- Designing and testing analytics and AI\-enabled solutions side\-by\-side with SOF stakeholders and demonstrating capabilities via robust campaigns of learning.
- Identifying and mitigating operational environment constraints, such as limited bandwidth at the tactical edge and contested environments early in the development process.
- Supporting the establishment of clear pathways to adopt capabilities deemed successful after rigorous testing to ensure sustainment and wide\-scale adoption.
- Utilizing appropriate code share repositories (e.g., GitHub) for all development activities.
*This position is contingent on contract award.*
Work Environment:
- Moderate noise (i.e. business office with computers, phone and printers) and /or occasional Loud noise (airfield, large equipment).
- Ability to sit at a computer terminal for an extended period of time.
Physical Demands:
- While performing the responsibilities of the job, the employee is required to sit, stand, talk, and hear.
- Employee is often required to sit and use their hands and fingers to operate a computer.
- Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Travel:
- 0\-10% / Minimal travel.
WWC Global, an operating firm of Command Holdings, is a tribally\-owned firm providing management consulting services to U.S. government agencies.
Pursuant to PL 93\-638, as amended, preference will be given to qualified Native Americans and spouses in all phases of employment.
At WWC Global, our employees are the embodiment of our success as a firm. Our team is comprised of a tenacious group of professionals located across the globe. It includes military veterans and spouses of active duty troops, former federal employees, policy experts, academics, attorneys, and technical and business experts, all of whom share a strong work ethic and the skills to succeed in both collaborative and independent environments. WWC Global is invested in the long\-term success of both our clients and colleagues for the right reasons. Our dedication to putting good government into practice is underpinned by a merit\-based culture that measures success by productivity and credibility.
WWC Global will provide reasonable accommodations to applicants who are unable to utilize our online application system due to a disability. Please send your request to the Human Resources Team (mailto:[email protected]).
WWC Global is committed to equal employment opportunity based on merit. We recruit, employ, train, compensate, and promote without regard to race, religion, color, national origin, age, sex, disability, protected veteran status, or any other basis protected by applicable federal, state, or local law, and in accordance with EO 14173 (https://www.whitehouse.gov/presidential\-actions/2025/01/ending\-illegal\-discrimination\-and\-restoring\-merit\-based\-opportunity/) and Federal Employment Laws: Equal Employment Opportunity and Employee Polygraph Protection Act.
WWC Global’s Affirmative Action Program is available to any employee or applicant for employment for inspection upon request, to the extent required by federal regulations. The Affirmative Action Program can be accessed during normal business hours by making an appointment with the Human Resources Team (mailto:[email protected]).
Basic Requirements
- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a related technical field.
- Minimum of five (5\) years of experience in machine learning engineering, data science, or a related technical discipline, including hands\-on experience designing, training, deploying, and monitoring ML models in cloud or hybrid production environments.
- Current, active TS/SCI security clearance.
- Outstanding communication skills, influencing abilities, and client focus.
- Professional proficiency in English is required.
- Demonstrated proficiency in using all Microsoft Office applications.
- Ability to access federal facilities in compliance with Real ID. More information about Real ID can be found here: https://www.dhs.gov/real\-id/about\-real\-id (https://www.dhs.gov/real\-id/about\-real\-id) and at https://www.tsa.gov/travel/security\-screening/identification (https://www.tsa.gov/travel/security\-screening/identification).
- Applicants must be currently authorized to work in the United States on a full\-time basis. WWC Global will not sponsor applicants for work visas for this position.
Preferred Requirements
- Master's degree or PhD in Machine Learning, Computer Science, Artificial Intelligence, Applied Mathematics, or a related field.
- Experience working in DoD or Intelligence Community environments.
- Experience developing and deploying AI/ML capabilities in support of SOF or other warfighting organizations.
- Experience with LLM/RAG development and GenAI operationalization in classified or restricted environments.
- Experience conducting AI/ML experimentation and multi\-lateral exercises to validate model performance against high\-impact use cases.
- Experience with low\-bandwidth and Disconnected, Intermittent, and Limited (DIL) environment constraints.
- One or more of the following: Databricks Certified Machine Learning Professional, AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate, TensorFlow Developer Certificate, or equivalent AI/ML platform certification.
Benefits
WWC Global offers a competitive benefits plan including:
- Health, Dental, and Vision Insurance
- Flexible Spending Accounts
- Life and Disability Insurance
- 401(k)
- Paid Time Off
- Paid Holidays
- Employee Assistance Program
- Pet Insurance
- Eligibility requirements apply
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 Command Holdings, 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.
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.
Command Holdings AI Hiring
Command Holdings has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tampa, FL, US.
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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