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About This Role
AI is transforming how brands and agencies create content at scale. Through an AI-first approach and deep expertise across first-party and third-party generative AI solutions, we help brands and agencies transform their end-to-end creative workflows from ideation to activation, to deliver AI-powered content services with speed, scale, and governance. We also function as a continuous product innovation engine, feeding repeatable customer solutions straight into the product roadmap to accelerate product-led growth.
This is a customer-facing value and advisory role. You will bring a strong understanding of how the end-to-end content supply chain operates, from ideation and production through activation and measurement, so you can advise customers' creative teams with credibility and be accountable for the value each engagement is designed to deliver.
WHAT YOU'LL DO
- Engage with customer creative orgs to learn their brand system, production model, and the metrics leadership cares about
- Communicate with executives through outcomes and insights (not status updates), and with designers, editors, and creative directors in their own language
- Define and prioritize high-impact use cases with stakeholders: build the value hypothesis, hold engagements accountable to it, and measure/communicate progress toward realized outcomes
- Advise on deploying and scaling AI-powered creative pipelines across the end-to-end content supply chain, helping customers grow from one proven workflow to full production volume
- Recognize and mitigate risks from ill-defined use cases, insufficient scale, and customer readiness gaps
- Understand what creative teams care about and give honest guidance even when it's not what they want to hear
- Facilitate workshops that help creative teams map their production flow to AI workflows
- Bring in creative technology, engineering, product, and program specialists as needed
- Keep cross-functional teams aligned and synthesize customer feedback for product teams
- Train customer teams and transition ownership cleanly so value continues after the engagement ends
- Map future-state AI-powered workflows to current-state processes and roles, and build a change-management plan
- Drive solution adoption through enablement sessions, demos, and partnership with Customer Success and Enablement teams
WHAT YOU'LL NEED
- A strong track record of owning business outcomes with enterprise customers, rather than simply completing tasks and moving on
- Real instinct and confidence for building and sustaining trust with executives, including a willingness to reach out boldly rather than waiting to be looped in
- A self-directed style that creates momentum proactively, since this role rewards leading from the front over passive participation
- Concrete examples of past work that translated into measurable business impact for a client
- Hands-on experience with creative production workflows or content supply chains, ideally gained at enterprise companies or agencies
- A background in creative operations, digital asset production, or content supply-chain management, specifically, is a plus
- A genuine, end-to-end understanding of how the content supply chain works, from strategy and planning through production, activation, and measurement
- Enough comfort with technology and product to hold credible conversations about feasibility and capability, ideally including hands-on experience in the Adobe ecosystem (Creative Cloud, Firefly, or GenStudio) or working knowledge of AI creative platforms like ComfyUI, Runway, or Claude Design
- Experience working within an agile, pod-based software development lifecycle
- Strong influence and facilitation skills, since this role involves coordinating specialists and cross-functional teams without direct authority over them
- Good judgment for knowing when a situation calls for bringing in deeper expertise rather than trying to solve it solo
- Bonus points for familiarity with specific creative workflows such as Assets at Scale, 3D, video, or product-asset production, or broader experience with AI-enabled workflow transformation in a creative or content-operations setting
- Willingness to travel within the US (10–20%)
ABOUT US
Born in 2001, Code and Theory is a digital-first creative agency that sits at the center of creativity and technology. We pride ourselves on not only solving consumer and business problems, but also helping to establish new capabilities for our clients. With a global client roster of Fortune 100s and start-ups alike, we crave the hardest problems to solve. We have teams distributed across North America, South America, Europe, and Asia. The Code and Theory global network of agencies is growing and includes Kettle, Instrument, Left Field Labs, Create Group, Current, and TrueLogic.
Striving never to be pigeonholed, we work across every major category: from tech to CPG, financial services to travel & hospitality, government and education to media and publishing. We value the collaboration with our client partners, including but not limited to Adidas, Amazon, Con Edison, Diageo, EY, J.P. Morgan Chase, Lenovo, Marriott, Mars, Microsoft, Thomson Reuters, and TikTok.
The Code and Theory network is comprised of nearly 2,000 people with 50% engineers and 50% creative talent. We're always on the lookout for smart, driven, and forward-thinking people to join our team.
The compensation range for this role is $145,000 - $200,000 and spans multiple levels. We're open to hiring at the level that best matches the right candidate's experience. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, budget, and location.
Salary Context
This $145K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Code and Theory, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($172K) sits 19% below the category median. Disclosed range: $145K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Code and Theory AI Hiring
Code and Theory has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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