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About This Role
About OUTFRONT
We are one of North America’s most innovative media companies. We leverage the power of creative excellence, unbeatable locations and smart audience data to change the game for advertisers. Our purpose as a company is to help people, places and businesses grow stronger. To do this, we make meaningful connections between brands and people when they are outside of their homes through one of the largest and most diverse sets of out\-of\-home assets including billboards, transit and mobile displays across the U.S. We connect diverse audiences across over 150 markets and conduct our business considering all our stakeholders, from clients and employees, to the communities where we operate. We are committed to creating a diverse and inclusive work environment that promotes the growth of our people. Come join our industry\-leading team!
What We Offer
OUTFRONT offers a comprehensive benefits program including:
- Medical, Dental, Vision (including same and opposite\-sex domestic partners)
- HSA and FSA plans, Family Benefits, Pet Benefits
- 401(k) Plan with an Employer Match
- Paid Time Off, Commuter Benefits, Educational Assistance
- Robust Diversity, Equity and Inclusion program including 7 Employee Resource Groups (ERGs)
About the Role
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We're hiring our first dedicated owner for OUTFRONT's internal AI platform. Today, our internal AI assistant (OUTGPT, built on the Vercel AI SDK and AWS Bedrock) is moving into production, and our external agent infrastructure (the Agency Connect MCP server, which exposes OUTFRONT inventory and campaign tools to AI buyer agents) is live with early partner adoption. Both need a senior engineering owner, and both need to grow into the foundation of how OUTFRONT works in an AI\-native era.
This is a hands\-on technical leadership role. You'll own the architecture, write production code, set the direction for our AI platform, and partner closely with our CTO and VP of AI on what comes next. You'll also work with our newly forming automation team, providing the platform spine they build on top of.
If you've wanted to build a meaningful AI platform from the ground up inside a company that's actually committed to it, this is that role.
What You'll Work On
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- OUTGPT. Take ownership of our internal AI platform, built on the Vercel AI SDK and AWS Bedrock. Move it into full production, build the roadmap, expand model and agent capabilities, integrate with internal data sources, and make it the default surface our 75\-person tech org and broader business reach for.
- Agency Connect MCP. Evolve our Model Context Protocol server that exposes OUTFRONT's inventory, holds, and campaign tools to AI buyer agents. This is OUTFRONT's external bet on agent\-native ad buying, and it's live.
- Agent infrastructure. Design and build the shared platform that internal automation teams use to ship agents safely: model routing, evaluation, guardrails, observability, secrets handling, cost controls.
- AWS Bedrock and AgentCore. Be our deepest voice on the AWS AI stack. Evaluate, integrate, and operationalize new capabilities as they ship.
- Architectural authority. Own the technical decisions that span our AI surface area, from how OUTGPT integrates with Salesforce and Snowflake to how Agency Connect MCP scales with partner adoption.
- Mentorship. Set the engineering bar for the AI \& Automation pod (two engineers and an intern joining behind you). Review their designs, raise their ceiling.
What We're Looking For
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- 7\+ years building production backend or full\-stack systems, with at least 2 years building LLM\-backed systems that real users depend on. Prototypes don't count here. We need someone who's operated agents and AI services in production and knows where the failure modes live.
- Deep AWS experience. Bedrock specifically is a strong plus. Comfort with Lambda, ECS, App Runner, RDS, and the IAM and networking surface that AI workloads require.
- TypeScript/Node and/or Python proficiency. Our AI platform is TypeScript\-forward (Vercel AI SDK, Next.js); Python shows up in agent infrastructure and integrations.
- Hands\-on experience with agent frameworks and protocols (MCP, A2A, or equivalent) or a clear track record of evaluating and adopting emerging frameworks quickly.
- Strong API design instincts. You've designed APIs that both humans and agents consume, and you understand why those are different problems.
- You've owned a platform that other engineers built on top of, and you understand the difference between shipping a feature and shipping a capability.
- You can sit with a non\-technical partner (a business stakeholder, a buyer\-side agency) and turn a fuzzy problem into a working system. Communication and judgment matter as much as the code.
Nice to Have
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- Experience in advertising technology, particularly programmatic, OOH, or DOOH.
- Familiarity with the M365 / Power Platform surface, since our automation pod operates there for personal\-productivity workflows.
- Experience with Snowflake, Sigma, or modern analytics stacks.
- Open source contributions to MCP, agent frameworks, or related ecosystems.
Tech Stack You'll Touch
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AWS (Bedrock, AgentCore, Lambda, ECS/App Runner, RDS, Cognito), TypeScript/Node, Vercel AI SDK, Next.js, Python, PostgreSQL, MCP, Salesforce, Snowflake, Bitbucket, Kiro, Claude Code.
Reporting and Team
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Reports to the VP of AI. Partners closely with the CTO, who is the current technical lead on OUTGPT and will remain a hands\-on collaborator. Works alongside a Senior Solutions Engineer and Solutions Engineer focused on internal business automation, plus an intern.
For New York, New Jersey, and California, the salary range for this role is $175,000\-$200,000 per year. Compensation is determined during our interview process by assessing a candidate’s experience and skills relative to internal peers and market benchmarks evaluated for the scope and responsibilities of the position. Please note that the foregoing compensation information is a good\-faith assessment associated with this position only and is provided pursuant to the corresponding Salary Transparency Laws.
### To all Recruitment Agencies: OUTFRONT Media LLC does not accept agency and unsolicited resumes. Please do not forward resumes to our OUTFRONT Media employees or any other company location.
OUTFRONT Media is not responsible for any fees related to unsolicited resumes.
OUTFRONT Media Is An Equal Opportunity Employer
All applicants shall receive equal consideration without regard to race, color, religion, gender, marital status, gender identity or expression, sexual orientation, national origin, age, veteran status or disability. Please refer to the OUTFRONT Media Affirmative Action policy statement.
Salary Context
This $175K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Outfront Media, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($187K) sits 5% above the category median. Disclosed range: $175K to $200K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Outfront Media AI Hiring
Outfront Media has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $145K - $200K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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