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About This Role
Job Description
Marketing Statement:
Metro's Technical Training and Development is hiring Operations Support Specialists to work alongside our Program Managers. These roles are the operational backbone of the team, handling day\-to\-day coordination with program managers. While each role has a different focus, both are rooted in work optimization, problem\-solving, and the support of instructors and trainees.
One role will support Facilities and Classroom Operations. This person ensures training spaces are ¿room ready¿ by managing classroom setup, technology, and service coordination so instruction runs without disruption. They will help move us from ad hoc problem solving to standardized systems, bringing structure, visibility, and consistency to how our learning environments operate.
The other will support Student Support and Program Operations. This person helps maintain, strengthen and support trainee experiences, coordinating outreach efforts, and managing the administrative processes that keep programs running and students on track.
Across both roles, we are looking for talented individuals who are organized, responsive, and solutions\-oriented.
Minimum Qualifications
Education
High school diploma or GED.
Experience
Four (4\) years of experience supporting operations, facilities, student services, program administration, or recruitment/event coordination in a training or educational environment.
Preferred
- Associate degree or higher in Facilities Management, Information Technology, Business Administration, or related field.
- Certifications in facilities support, AV technology, or customer service.
Medical Group
Satisfactorily complete the medical examination for this position, if required. The incumbent must be able to perform the essential functions of this position either with or without reasonable accommodations.
Job Summary
The Technical Training Operations Support Specialist provides comprehensive operational support to WMATA Technical Training and Development programs across facilities, technology, and student engagement functions. This role ensures that learning environments are functional, well\-equipped, and supportive of instructional and participant needs. Specialists may focus on different operational workstreams, such as classroom/facility support, student and participant engagement, or instructional technology, but all contribute to maintaining safe, efficient, and high\-quality training operations.
Essential Functions
- Prepare, organize, and maintain training environments, ensuring technology, equipment, and instructional resources are functional, available, and ready for use.
- Serve as primary point of contact for instructors, students, and staff; respond to operational, technical, or facilities\-related inquiries and resolve issues promptly to ensure uninterrupted training delivery.
- Monitor facility and classroom safety, cleanliness, and readiness; escalate maintenance or access concerns to appropriate teams.
- Set up and test AV systems, computers, and classroom technology for training sessions and events.
- Track and manage inventory of supplies, equipment, and technology assets; coordinate repairs or replacements as needed.
- Support student onboarding and participation by coordinating registration, access, and distribution of training resources.
- Maintain accurate documentation of classroom usage, student records, work orders, and inventory within designated systems (e.g., LMS, Maximo, PeopleSoft).
- Collaborate with program managers and instructors to ensure operational efficiency, participant readiness, and smooth delivery of training.
- Assist with logistics for orientations, special events, or space reconfigurations.
- Uphold health, safety, and confidentiality standards across all operational functions.
Other Functions
- Maintains and promotes awareness and accountability with safety policies and procedures while performing job functions. Promotes a positive safety culture and encourages reporting of safety concerns consistent with our Agency Safety Plan, other regulatory requirements within the Safety Management System and just culture principles.
The functions listed are not intended to limit specific duties and responsibilities of any particular position. Nor is it intended to limit in any way the right of managers and supervisors to assign, direct and control the work of employees under their supervision.
Evaluation Criteria
Consideration will be given to applicants whose resumes demonstrate the required education and experience. Applicants should include all relevant education and work experience.
Evaluation criteria may include one or more of the following:
- Skills and/or behavioral assessment
- Personal interview
- Verification of education and experience (including certifications and licenses)
- Criminal Background Check (a criminal conviction is not an automatic bar to employment)
- Medical examination including a drug and alcohol screening (for safety sensitive positions)
- Review of a current motor vehicle report
Closing
WMATA is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other status protected by applicable federal law.
This posting is an announcement of a vacant position under recruitment. It is not intended to replace the official job description. Job descriptions are available upon confirmation of an interview.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Washington Metropolitan Area Transit Authority, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Washington Metropolitan Area Transit Authority AI Hiring
Washington Metropolitan Area Transit Authority has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span MD, US, Washington, DC, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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