Interested in this AI/ML Engineer role at Shutterstock?
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Why this role matters
AI is only as good as the data that trains and powers it. As Head of Marketing for our Data Licensing \& AI Services, you will leverage Shutterstock’s world\-class library of over 1 billion ethically sourced assets along with its growing list of AI partners and services offerings to position us as the premier foundational infrastructure partner for the world’s leading tech companies and LLM developers. You aren't just marketing a product; you are shaping the future of ethical AI.
This position requires a strategic thinker with a deep understanding of market dynamics and customer needs, as well as experience in B2B content and digital solutions. The ideal candidate will be comfortable developing high\-impact strategies, identifying market opportunities, and working cross\-functionally to bring those strategies to life and possess a deep understanding of the AI model training market, AI and ML technologies, trends, and applications.
Impact Outcomes
- Architect the GTM Playbook:Design, launch, and scale the global marketing blueprint for the AI Data Licensing business unit, transforming high\-level business goals into measurable market share.
- Accelerate Enterprise Revenue: Build a high\-velocity demand generation and lead\-nurturing engine that directly fuels the enterprise sales pipeline and unlocks new channel partnerships.
- Define the Category Market Fit: Turn deep industry analysis, competitor tracking, and AI/ML customer insights into actionable growth strategies that capture untapped market segments.
- Orchestrate Global Campaigns: Lead the lifecycle execution of targeted marketing campaigns that demonstrably move the needle on new customer acquisition and long\-term retention.
- Establish a Data\-Driven Culture:Build out the marketing tech stack, attribution models, and KPIs needed to relentlessly optimize campaign performance and maximize ROI.
- Unify the Brand Matrix: Act as the ultimate cross\-functional bridge, seamlessly aligning the AI division's specialized goals with central Shutterstock brand, product, and operational resources.
- Command Industry Thought Leadership: Position Shutterstock as the definitive voice in ethical AI training data by securing tier\-one speaking tracks, designing high\-impact industry events, and producing elite B2B content.
- Influence Executive Strategy:Serve as the core marketing stakeholder for the business unit, delivering data\-backed insights directly to senior leadership to shape the future direction of the company.
What You’ll Bring to the Role:
- Experience: 7\-12 years in B2B marketing with a focus on content, technology, data, AI and/or digital media solutions, including experience in lead generation, establishing and nurturing channel partnerships, customer acquisition, and lifecycle management.
- Strategic Vision: Proven experience in developing and executing comprehensive and effective marketing strategies that drive business growth and customer retention.
- Analytical Skills: Proficiency in marketing analytics tools (e.g., Google Analytics, CRM, and marketing automation systems) and a data\-driven approach to campaign optimization.
- Collaborative Leadership: Exceptional communication and presentation skills with a track record of successful cross\-functional collaboration, particularly with revenue, product, and customer advocacy teams.
- Market Insight: Strong understanding of market dynamics, customer needs, and competitive positioning within the content solutions industry.
- Education: Bachelor’s degree in Marketing, Business, or a related field; advanced degree preferred.
Why Shutterstock:
- You have a direct impact on the success of the company. Your work matters and is essential to the evolution of our growing AI Business.
- Executive leadership cares personally. They prioritize growth and planning your career path with your goals and passions in mind.
- Flexibility to work between home and office with everything you need to be successful in both
- A generous and competitive benefits package.
Shutterstock connects diverse artists and creative professionals around the globe with the agencies, brands and people who need their work and services. It’s a place where creators come to be inspired and discover new ways to produce their best work.
Shutterstock enables its employees to drive culture and tap into the world around them to develop the toolbox and solutions that help others share their world views. At Shutterstock, your ideas will be welcomed, your uniqueness will be celebrated, and you will be supported in presenting your view of the world as you experience it. We’re champions of resiliency; quickly learning from our shortcomings in our pursuit of continuous growth.
Diverse teams are critical to our success. We encourage people from different backgrounds to apply and we commit to creating and maintaining a culture where employees know they belong and have equal opportunities to succeed.
\#LI\-MS1
\#LI\-Hybrid
At Shutterstock, we are committed to providing competitive pay and benefits that are in line with industry standards. The compensation package offered may vary depending on employment experience, skills, and knowledge. You may also be eligible for our generous benefits package including health, wellness and financial benefits. Compensation ranges for candidates in locations outside of the location(s) below may differ based on the cost of labor, market, and additional factors.
The pay range for this position is below:
- 210,000 \- 245,000 per year in New York
Note: Commission\-eligible roles are expressed as on\-target earnings (base and commission). Non\-commission roles are expressed as base salary only but are also eligible for annual incentives.
Shutterstock Values
We are one team collectively focused on creating an unrivaled experience for our Customers and Contributors. Our Values represent the mindset of the employee who will thrive at Shutterstock. If you are passionate about what you do, and want to become part of a cutting\-edge technology company building industry leading products, please apply.
Shutterstock is an Equal Opportunity Employer. Suitably qualified and eligible candidates are encouraged to apply regardless of age, color, disability, national origin, ancestry, race, religion, gender, sexual orientation, gender identity and/or expression, veteran status, genetic information, or any other status protected by applicable law.
Shutterstock ensures that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Persons with disabilities who anticipate needing accommodations for any part of the application process may contact, in confidence, accommodation\[email protected].
The referenced salary range is based on the Company’s good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.
Salary Context
This $210K-$245K range is above the 75th percentile 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 Shutterstock, 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. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $210K to $245K.
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.
Shutterstock AI Hiring
Shutterstock has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $245K - $245K.
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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