Interested in this AI/ML Engineer role at Human Resource Certification Institute?
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About This Role
- *This position may be performed remotely, however, occasional travel to the headquarters office is required (2 times/year).*
*\*\* Applicants MUST be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa.*
Please apply online at https://www.hrci.org/about\-hrci/hrci\-careers.
Who we are:
HRCI®, headquartered in Alexandria, Virginia, is the premier credentialing and learning organization for the human resources profession. For over 50 years, we have set the global standard for HR expertise and excellence through our commitment to the development and advancement of businesspeople in the people business. HRCI develops and offers world\-class learning, as well as the administration of eight global certifications and is dedicated to helping professionals achieve new competencies that drive business results. We are a dynamic non\-profit with a hard\-working, committed team. Want to know more about what life is like at HRCI? Find out more about us at https://www.hrci.org/about\-hrci/life\-at\-hrci.
Why HRCI:
- We are a Washington Post Top Workplace, two years running (2024 \& 2025\)!
- Be part of a dynamic and passionate team of professionals focused on driving the organization forward.
- Have the autonomy to grow your career and opportunities for professional development.
- Highly rewarding work in a fun, team\-oriented culture.
- Great benefits (health insurance, generous time off, 401(k) match, parental leave) and a flexible working environment.
Position Overview:
This role owns the infrastructure that lets our engineers ship faster with Claude Code, builds the environments that let non\-technical teams automate their own work with Cowork, and keeps our digital agents governed and running. Security and cloud infrastructure are core to the job, and the expectation is that Claude accelerates that work enough to free capacity for everything else described here.
Automation \& Efficiency
- Configure our Claude account to balance benefits and usage costs – we want to optimize net benefits of working with Claude
- Scaffold and maintain Claude Code environments for engineering
- Own the governance layer for AI agents operating within HRCI systems
- Set up Cowork environments so go\-to\-market, operations, finance, and other non\-technical teams can automate workflows without routing every request through engineering
- Maintain and extend CI/CD pipelines for code deployment
- Improve deployment processes to reduce errors and increase velocity
- Maintain development, staging, and production environments
- Maintain and organize code repository documents including provisioning npm and NuGet repos
Infrastructure \& Cloud
- Configure, monitor, and maintain Azure services and virtual machines
- Build and manage containerized applications with Azure Container Apps
- Migrate workloads from VMs to containers
- Troubleshoot system performance issues and implement reliability improvements
- Execute all of the above through control panels or scripts
- Research and recommend new Azure services to improve reliability and reduce costs
- Learn and maintain working knowledge of our core SaaS platforms (eCommerce, CRM, Office 365, collaboration tools, contact center, etc.)
- Arrange users, settings and permissions across platforms
Security
- Harness Claude across our environments to identify and fix security vulnerabilities
- Monitor and respond to the threat landscape for coding agent\-enabled attack patterns: prompt injection, supply chain attacks through AI\-generated code, and adversarial use of the same agentic tools our engineers use
- Maintain endpoint protection, alerts and patch management
- Manage our organization’s security configurations, automated pen testing platform and security training software
The Skills You Need to Succeed:
- Demonstrated experience with AI coding agents in production environments
- 3\+ years software engineering either front end or back end
- 3\+ years hands\-on experience with Azure (VMs, App Services, networking, storage, identity)
- 3\+ years experience with CI/CD pipelines and DevOps practices
- 3\+ years experience with Azure DevOps and Azure Repos
- 3\+ years experience with GitHub
- 3\+ years experience with containerization
- Ability to use Claude Code in a real codebase, including CLAUDE.md and skills authoring and MCP configuration — or comparable work with another agentic coding tool and a clear ability to transfer that experience
- Understanding how agentic coding tools navigate codebases: file traversal and context windows
- Security instincts specific to AI: understands the risk surface of giving agents tool access, recognizes prompt injection as an attack vector, and thinks about what adversaries can do with coding agents
- Can communicate clearly with both engineers and non\-technical teams — this role works across the full company
- Strong scripting in Python
- Knows Azure (VMs, App Services, storage, identity, containers, DevOps CI/CD pipelines)
- Solid networking fundamentals: DNS, TCP/IP, firewalls, VPNs
- Can manage Windows Server and Linux systems
- Bachelor's degree in Computer Science, Information Technology, or related field—or equivalent practical experience
*Salary range: $110,000 \- 140,000/year; Compensation is based on a variety of factors including, but not limited to, relevant experience, skills, and internal equity.*
Salary Context
This $110K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Human Resource Certification Institute, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($125K) sits 31% below the category median. Disclosed range: $110K to $140K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Human Resource Certification Institute AI Hiring
Human Resource Certification Institute has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alexandria, VA, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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