Interested in this AI/ML Engineer role at Outfront Media?
Apply Now →Skills & Technologies
About This Role
About OUTFRONT
OUTFRONT is one of America’s leading IRL media companies, built to do more than be seen. We create breakthrough real\-world experiences that engage, influence, and drive impact. Through creative excellence, high\-impact environments, and intelligent audience data, we help brands move people, show up with relevance, and make a lasting impression.
Our media lives where life happens, embedded in the culture and landscape of cities and the flow of everyday movement. It’s trusted, unavoidable, and part of the cultural fabric, creating meaningful connections between brands and the audiences they serve.
We’re redefining out of home by bringing intelligence, flexibility, and digital innovation to scale, turning physical presence into measurable impact and real\-world influence. 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
------------------
We're hiring an AI Automation Engineer to grow into one of the most interesting roles in our technology org. You'll partner with our Senior AI Automation Engineer to embed with business teams across OUTFRONT, find broken manual processes, and ship AI\-powered automations that fix them. You'll spend your first year ramping fast: shipping production work from day one, learning our AWS and Microsoft stacks, building a reputation as someone who delivers.
This is an engineering role first. We're hiring for software fundamentals (you can write good code, design clean APIs, debug a real production issue) and we'll teach you the rest. Power Platform, our internal AI tooling, OOH advertising — none of it is a prerequisite. We assume you'll pick those up. What we can't teach you in 90 days is the engineering chops.
You'll also contribute to OUTGPT, our internal AI platform built on the Vercel AI SDK and AWS Bedrock. Early on, that means small features and integrations under review. As you grow, you'll own larger pieces.
What You'll Work On
-----------------------
- Automation delivery alongside the Senior. Pair on stakeholder engagements early. Take the lead on your own projects as you ramp. Ship automations that real business teams use every day.
- AI agents and workflows. Build agents in Copilot Studio for M365\-native use cases and in Bedrock for autonomous and system\-data work. Ship, learn where they break, ship better ones.
- OUTGPT contributions. Work on features, integrations, and capabilities for our internal AI platform. Your code gets reviewed by the Lead AI Platform Engineer; your bar comes up fast.
- Integration work. REST APIs, webhooks, OAuth, SQL, Snowflake, Salesforce. The connective tissue of every automation we ship.
- Mentorship in both directions. Get mentored by the Senior and the Lead. Mentor our intern. Both will make you better.
What We're Looking For
--------------------------
- 2–4 years building production software. Internships count toward the floor if the work was real and the systems shipped.
- Strong software fundamentals. You write clean code, you reason about systems, you can debug a problem you didn't write.
- Hands\-on experience with AI/LLMs, whether through professional work, side projects, hackathons, or coursework. You've built something that uses an LLM and learned why it's harder than it looks.
- Practical AWS experience. Lambda, S3, RDS, IAM at minimum. You don't need to be a cloud expert; you do need to be able to ship there.
- Proficiency in Python, TypeScript/Node, or both. Comfort with SQL.
- API integration experience. REST, JSON, OAuth basics. You've connected systems and seen things break.
- You want to be in front of customers. You're comfortable sitting with a Finance lead or a Sales Ops manager, asking dumb questions, and walking away with a plan.
- A portfolio that speaks for itself. GitHub, a side project, a deployed app, a meaningful internship — something we can look at and say "this person ships."
Nice to Have
----------------
- Experience with Microsoft Power Platform (Copilot Studio, Power Apps, Power Automate). If you don't have it, you'll learn it.
- Familiarity with the Vercel AI SDK, Next.js, or modern TypeScript stacks.
- Experience with MCP, A2A, or agent frameworks.
- Snowflake, Sigma, or modern analytics stack exposure.
- Experience in advertising technology, particularly OOH or DOOH.
- CS degree or equivalent technical degree. Not required if your portfolio is strong.
Tech Stack You'll Touch
---------------------------
AWS (Bedrock, Lambda, Step Functions, RDS), TypeScript/Node, Python, Vercel AI SDK, Next.js, PostgreSQL, Microsoft (Copilot Studio, Power Apps, Power Automate, M365\), Snowflake, Salesforce, Bitbucket, Kiro, Claude Code.
Reporting and Team
----------------------
Reports to the VP of AI. Works alongside the Lead AI Platform Engineer (technical mentor), the Senior AI Automation Engineer (your day\-to\-day pairing partner), and an intern (your first mentee).
For New York, New Jersey, and California, the salary range for this role is $125,000\-$145,000 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 $125K-$145K range is in the lower quartile 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. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($135K) sits 25% below the category median. Disclosed range: $125K to $145K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.