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About This Role
HOW YOU’LL MAKE AN IMPACT
USGBC and GBCI are expanding the use of Artificial Intelligence across internal operations, certification workflows, digital customer experiences, and enterprise data intelligence. As a Senior AI Platform Developer, you will design, build, and operationalize AI\-powered applications that support both staff and customers.
This role combines hands\-on AI engineering with web application development and platform operations. You will work closely with digital, product, data, and business teams to implement AI initiatives, including certification\-review assistant tools, conversational data\-insight tools, customer\-facing guidance systems, and knowledge intelligence platforms.
The role will help establish the technical foundation for scalable, secure, and responsible AI adoption across the organization while enabling teams to integrate AI capabilities into web platforms and digital products.
Key Responsibilities* Design and develop AI\-powered applications supporting internal staff and external customers.
- Build web applications, chatbots, and conversational interfaces that leverage large language models and AI services.
- Develop conversational AI systems using LLM architectures and retrieval\-augmented generation (RAG).
- Build and maintain APIs and microservices that enable AI capabilities across digital platforms.
- Develop AI\-powered chatbots and assistants to support initiatives such as certification\-review tools, customer guidance systems, and conversational analytics.
- Integrate AI solutions with enterprise platforms, including Snowflake, Salesforce, Power BI, Drupal, and other internal systems.
- Design knowledge ingestion and retrieval pipelines using embeddings, vector search, and semantic retrieval techniques.
- Operationalize AI systems with monitoring, logging, evaluation frameworks, and cost optimization.
- Coordinate closely with other developers, architects, product owners, and business stakeholders to deliver AI initiatives.
- Collaborate with cross‑functional teams to identify high‑impact AI use cases and deliver scalable production solutions.
- Ensure responsible AI practices, including citation grounding, security, governance, and data protection.
REQUIRED QUALIFICATIONS
Experience* 6–10\+ years of software engineering, platform development, or web application development experience.
- 3\+ years working with AI/ML or LLM\-based systems.
- Hands\-on experience building AI applications and chatbots using AWS services, particularly Amazon Bedrock.
- Experience building web applications or conversational interfaces integrating AI capabilities.
- Experience integrating foundation models such as Anthropic Claude or similar models into production workflows.
- Experience implementing retrieval\-augmented generation (RAG) or semantic search systems.
- Experience building APIs, microservices, and backend services supporting AI\-powered applications.
- Experience working collaboratively within engineering teams to deliver complex technical initiatives.
- Experience integrating AI solutions with enterprise systems and data platforms.
Education* Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field (or equivalent experience).
Technology/System(s)* AWS services include Amazon Bedrock, Lambda, S3, API Gateway, Step Functions, DynamoDB, and OpenSearch.
- Programming languages such as Python, Node.js, or similar backend technologies.
- Experience building AI\-driven web interfaces using frameworks such as React, Streamlit, or similar.
- Vector databases and embedding pipelines for semantic search.
- Data platforms such as Snowflake.
- API and microservices architecture.
- Git\-based development workflows.
Skills* Strong system design and problem\-solving abilities.
- Ability to translate business problems into scalable AI solutions.
- Experience building production\-ready AI systems and web applications.
- Strong collaboration and communication skills.
- Ability to coordinate technical work across teams and initiatives.
- Curiosity and adaptability with rapidly evolving AI technologies.
Language* English
ABOUT OUR TOTAL REWARDS PACKAGE
Salary
Final compensation and benefits will be confirmed at the time of offer and may vary based on factors such as internal equity, relevant experience, qualifications, and employment status. Please note that salary negotiations will not extend beyond the top of the internal salary range.
Benefits
We offer you:* Competitive compensation
- 401(k) with employer matching
- Professional development reimbursement
- We offer a healthcare plan through Cigna that includes medical, dental, vision, and prescription drugs. USGBC covers 100% of the premiums and an HRA that will assist you and your dependents in reaching the in\-network medical deductible. You will only be responsible for the $300 individual / $600 family up front deductible for medical services before the employer\-funded HRA will process payments for your in\-network claims
- Generous paid time off (12 paid holidays, 9 paid personal sick days and based on career level either 2 to 3 weeks PTO), including operations closed for a full week between Christmas and New Year’s
- 6 weeks paid renewal leave after 7 years of continuous service
LOGISTICS
Location: This position is remote in the U.S. or hybrid
Work Schedule: Monday to Friday from 9:00 AM to 5:30 PM
EEO STATEMENT
The U.S. Green Building Council is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, national origin, age, sexual orientation, gender identity or expression, disability status, protected veteran status, or any other characteristic protected by law.
ABOUT US
U.S. Green Building Council (USGBC) is a mission\-driven nonprofit dedicated to accelerating and scaling the transformation of the built environment. Through LEED—the world’s most widely used green building rating system— and initiatives likes Greenbuild, the Center for Green Schools and advocacy, USGBC empowers professionals to drive market transformation that advances human and environmental health, climate resilience, and equity.
Green Business Certification Inc. (GBCI) is the world’s leading sustainability and health certification and credentialing body, independently recognizing excellence in performance. GBCI administers project certifications and professional credentials and certificates including LEED, PERFORM, SITES, and TRUE Zero Waste.
We are proud to be globally recognized for our leadership in green building, environmental performance, and sustainable development.
Our Global Impact* Over 120,000 LEED\-certified commercial projects worldwide
- Millions of square feet of certified healthy, efficient, low\-carbon space
- Recognition in 180\+ countries for innovation in green building and business practices
Why Join Us
At USGBC and GBCI, you’ll work alongside passionate, mission\-aligned professionals who care deeply about people, the planet, and progress. We offer:* A purpose\-driven, inclusive culture
- Opportunities to grow your career and take ownership of meaningful work
- A chance to make a measurable impact on global sustainability efforts
- We’re seeking team members who thrive in collaborative environments, are committed to excellence, and want to build lasting partnerships that drive change in the built environment.
Meet Our Leaders and Learn More about our Mission:* U.S. Green Building Council Leaders
- Green Business Certification Inc Leaders
Culture and Values Statement
Working together, each of us advances our mission by respecting all voices, trusting and supporting one another, excelling through collaboration and accountability, and continuously improving ourselves and our organization.
Salary Context
This $120K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At U.S. Green Building Council, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($130K) sits 41% below the category median. Disclosed range: $120K to $140K.
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.
U.S. Green Building Council AI Hiring
U.S. Green Building Council has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $140K - $140K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 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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