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Apply Now →About This Role
NBBJ is an award\-winning design firm recognized as a TIME100 Most Influential Company, a Fast Company Most Innovative Architecture Firm and a two\-time 2025 AIA National Honor Award recipient. These recognitions reflect our purpose\-driven approach that, fueled by ideas and a culture of collaboration, creates healthy buildings, strong communities and a resilient environment. That’s where you come in. With leading clients, diverse colleagues and offices in creative capitals around the globe, a career at NBBJ will inspire you to be extraordinary. You can learn more about our firm, see what it’s like to work here and explore recent projects and ideas at NBBJ.com. Join us to make an impact today!
The role at a glance:
The AI Adoption \& Knowledge Coordinator is an early\-career role on the Data \& AI team, serving as the firm's go\-to resource for staff questions about AI tools, workflows, and best practices. They work directly with staff who have day\-to\-day questions and coordinate the network of AI Champions in studios who are helping their colleagues build fluency and adopt new tools. The role maintains the knowledge base that documents how AI is being used firm\-wide and keeps training content and resources current to support both. As a member of the Data \& AI team, this role provides the connective layer that keeps adoption moving across the firm, enabling the rest of the team to stay focused on strategy, use case development, and innovation.
The AI Adoption \& Knowledge Coordinator will be part of NBBJ’s Studio N, a firmwide studio of thought leaders and experts, brought together as an interdisciplinary team to support innovative and sustainable design. Based in the US, this firmwide role will work closely with project teams in 12 global offices in the US, UK, and China. The preferred candidate will be based in our Seattle office; however, candidates will be considered in any of our NBBJ locations in the USA (Boston, Columbus, Los Angeles, New York, Portland, San Francisco or Seattle).
Why this role is important to NBBJ:
As NBBJ integrates AI tools across the design practice and business operations, the pace of adoption depends as much on communication, training, and documentation as it does on the technology itself. Staff need a reliable, approachable resource when they have questions, and the knowledge being generated needs to be captured and kept current. Adoption at scale also requires a network of champions in studios who can model best practices and support their colleagues locally, and that network needs coordination, current materials, and a clear connection back to the Data \& AI team. Without someone holding both together, documentation goes stale, training is duplicated, and knowledge stays siloed in studios rather than being shared across the firm. This role creates the conditions for increased adoption as the initiative grows.
Summary of Key Responsibilities:
*AI Support \& Staff Enablement*
- Serve as an accessible first point of contact for day\-to\-day staff questions about AI tools, workflows, and best practices across design and business functions, escalating to the AI Integration Lead or Data \& AI Lead when needed
- Coordinate the AI Champion network across studios and office locations, keeping champions connected, sharing resources and updates, and maintaining regular touchpoints to understand where questions and friction are emerging across the firm.
- Support the onboarding of new staff to the firm’s AI toolkit, standards, and resources, and maintain onboarding materials
*Knowledge Management \& Documentation*
- Develop and maintain a structured internal library of how\-to guides, explainers, FAQs, and use case documentation that reflects the full scope of the firm's Data \& AI initiative.
- Keep documentation current and accurate as tools, workflows, and firm standards evolve.
- Manage and maintain external training platforms, curated resource libraries, and third\-party learning tools the firm utilizes, ensuring content is organized, accessible, and up to date.
- Capture staff feedback across the firm to surface recurring questions, adoption gaps, and unmet needs back to the Data \& AI team.
*Training Coordination \& Support*
- Support and coordinate training sessions, including material preparation, scheduling, and follow\-up documentation, and develop resources that equip champions to facilitate learning in their own studios.
- Develop supplementary training resources, reference cards, and self\-service guides that allow staff to learn at their own pace between sessions.
- Coordinate with HR to keep AI training content current in NBBJ’s Learning Management System.
- Track engagement with training content and flag areas where additional support or updated materials are needed.
*Internal Communications*
- Support the Data \& AI Lead in maintaining a steady cadence of internal communications related to the initiative, including announcements, progress updates, and presentations, including the production and distribution of those communications.
- Produce clear, visually polished content that makes complex data or AI concepts accessible to a broad, non\-technical audience across the firm.
- Maintain a consistent voice and visual standard across all Data \& AI initiative communications.
*External \& Proposal Content*
- Support the production of client\-facing and proposal materials related to the firm’s AI capabilities, coordinating content development and ensuring materials are current and organized for pursuit teams to use.
- Stay current on how peer firms and adjacent industries are communicating their AI work, and bring relevant observations back to the Data \& AI Lead to inform firm positioning.
Required Qualifications
- 2 to 5 years of professional experience in architectural design, or a related design discipline.
- Hands\-on experience using generative AI tools across categories such as language, mixed\-media, and workflow automation, with genuine curiosity about how the space is evolving.
- A confident and adaptable educator who can explain unfamiliar concepts clearly, read a room, and adjust their approach based on who they are working with.
- Exceptional communicator in writing and in person, with a strong instinct for making complex topics clear for a broad audience, including clients and non\-technical stakeholders.
- Strong graphic sensibility required; demonstrated ability to produce polished written and visual content.
- A self\-starter who can solve problems independently, figure out what is needed, and build structure where little exists.
Preferred Qualifications
- Experience developing training programs, curricula, or knowledge resources in a professional setting.
- Hands\-on experience using AI tools to accelerate content creation, with the ability to adapt as the toolset evolves.
- Familiarity with Figma or Adobe Creative Suite.
Seattle based is preferred but willing to consider Los Angeles, New York or other NBBJ Office Major City.
*The annual base pay range for this role dependent on location. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications, and experience, approximately $75,000\-85,000/year.*
Salary Context
This $75K-$85K 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 NBBJ, 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 in Demand for This Role
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. This role's midpoint ($80K) sits 63% below the category median. Disclosed range: $75K to $85K.
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.
NBBJ AI Hiring
NBBJ has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $85K - $85K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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