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About This Role
Company Description
VERSANT is a leading force in news, sports and entertainment \- home to iconic and trusted brands that inspire, inform, and delight audiences. Our unique combination of content, technology and services enriches the cultural fabric, igniting passions, sparking conversations, and connecting people to what they love most.
As an independent, publicly traded company, VERSANT brings together powerhouse cable networks \- including USA Network, CNBC, MS NOW (formerly MSNBC), Oxygen, E!, SYFY, and Golf Channel \- with dynamic digital and direct\-to\-consumer brands such as Fandango, Rotten Tomatoes, GolfNow, GolfPass, and SportsEngine. Together, these businesses reflect our commitment to delivering exceptional experiences across every screen and service.
VERSANT is an industry\-changing media company fueled by innovation and an entrepreneurial spirit. With a strong foundation and a forward\-looking vision, VERSANT empowers creativity, embraces change, and drives connection in an ever\-evolving world.
Job Description
As a Senior AI/ML Engineer on the AI Experience team, you will play a critical role in designing, building, and operationalizing intelligent systems that enhance content discovery, personalization, search, editorial workflows, and audience experiences across VERSANT digital platforms.
You’ll work with Python, Java, LangChain, AWS, OpenSearch, and a range of modern AI models to deliver scalable machine learning and generative AI capabilities. You will build services that support retrieval\-augmented generation, semantic search, model integration, prompt orchestration, evaluation, and production\-grade AI workflows.
You will contribute not only through strong engineering and applied AI expertise, but also through mentorship, architectural input, responsible AI practices, and a commitment to building reliable, measurable, and maintainable AI/ML\-enabled products.
What We Value
Senior AI/ML Engineers at VERSANT go beyond model experimentation. They turn ambiguous business needs into practical AI solutions, mentor teams on sound ML and software engineering practices, and help shape a culture of responsible innovation. They partner across product, editorial, data, and engineering teams to deliver systems that are reliable, scalable, observable, and aligned with business priorities.
Responsibilities
Leadership
- Partner with product, data science, editorial, analytics, and design teams to define clear, practical AI and ML requirements
- Mentor engineers through architecture reviews, design discussions, code reviews, pairing, and onboarding
- Promote engineering, ML, and responsible AI best practices across the team
- Identify and solve systemic team issues that impact delivery, model quality, reliability, or operational excellence
- Participate in and help guide conversations around AI architecture, emerging model capabilities, search relevance, and media industry trends
- Champion a user\-first mindset, advocating for AI solutions that improve content performance, editorial productivity, and audience experience
Technical
- Design and deliver end\-to\-end AI/ML services that support content intelligence, semantic search, recommendations, automation, and generative AI use cases
- Build and maintain production services using Python, Java, REST, and event\-driven patterns
- Develop AI applications and orchestration workflows using LangChain and related frameworks
- Integrate with large language models, embedding models, multimodal models, and domain\-specific AI services
- Design prompt engineering strategies, prompt templates, guardrails, and evaluation workflows for generative AI systems
- Use AWS services to deploy, monitor, secure, and scale ML\-enabled applications and data pipelines
- Implement search, vector search, and retrieval\-augmented generation patterns using OpenSearch and related indexing strategies
- Promote test coverage, model evaluation, secure development, and responsible AI practices
- Drive adoption of reusable AI platform patterns and help evolve shared ML engineering capabilities
- Debug, profile, and optimize model integrations, inference workflows, search relevance, latency, and cost
- Collaborate with SREs and platform teams to ensure AI services are observable, resilient, and production\-ready
Delivery
- Scope large AI initiatives and epics; break down complex model, data, and platform problems into actionable tasks
- Proactively communicate risks, timelines, blockers, model limitations, and operational tradeoffs
- Balance competing priorities from editorial, product, data, privacy, security, and platform teams
- Support the team in achieving realistic delivery timelines while maintaining code quality, model quality, and sustainable velocity
Communication \& Collaboration
- Partner across various teams to define and deliver cohesive AI\-powered experiences
- Participate in technical hiring to help scale AI, ML, and platform engineering capabilities
- Create and maintain documentation for AI architectures, model integrations, prompt strategies, evaluation results, and operational runbooks
- Communicate technical decisions, model behavior, risks, and tradeoffs clearly to both technical and non\-technical stakeholders
- Actively participate in post\-incident reviews, model quality reviews, and cross\-team discussions
Qualifications* Strong software engineering and applied AI/ML experience in large\-scale, high\-availability environments
- Proficiency in Python and Java for building production services, data workflows, and ML\-enabled applications
- Hands\-on experience with LangChain or comparable AI orchestration frameworks
- Experience integrating and evaluating a variety of AI models, including large language models and embedding models
- Deep understanding of prompt engineering, retrieval\-augmented generation, semantic search, and model evaluation patterns
- Experience with AWS cloud services and production deployment patterns for AI/ML workloads
- Experience with OpenSearch, search relevance, indexing strategies, and vector search capabilities
- Proven ability to write secure, maintainable, testable code and operate services in production
- Experience working in agile teams with strong delivery and collaboration practices
- Strong understanding of observability, performance tuning, cost optimization, and operational reliability for AI systems
- Excellent problem\-solving, debugging, analytical, and communication skills
Preferred:
- Experience in the media, publishing, streaming, advertising, or digital content industry
- Familiarity with content modeling, taxonomies, metadata, editorial workflows, personalization, and recommendations
- Experience with MLOps, model monitoring, feature pipelines, experimentation, A/B testing, or offline/online evaluation frameworks
- Master’s degree in computer science, machine learning, data science, or equivalent experience
What We Offer
At our Englewood Cliffs campus, you’ll have access to:
- Free onsite fitness center and daily workout classes
- Gourmet cafeteria and fresh daily meal options
- Onsite services like dry cleaning and shoe repair
- Free shuttle service to/from Manhattan, Hoboken, and Jersey City
This position is eligible for company\-sponsored benefits including medical, dental, and vision insurance, 401(k), paid time off, tuition reimbursement, and more.
Additional Information
As part of our selection process, external candidates may be required to attend an in\-person interview with a VERSANT Media employee at one of our locations prior to a hiring decision. VERSANT Media's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law.
If you are a qualified individual with a disability or a disabled veteran and require support throughout the application and/or recruitment process as a result of your disability, you have the right to request a reasonable accommodation. You can submit your request to [email protected].
VERSANT Media is committed to fair and equitable compensation practices. We include a good faith pay range for each position to comply with applicable state and local pay transparency laws and to promote equity across our organization. Actual compensation will be based on factors such as the candidate's skills, qualifications, experience, and location and may include additional forms of compensation and benefits such as health insurance, retirement plans, paid time off, etc.
*VERSANT Media is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at VERSANT via\-email, the Internet, or in any form and/or method without a valid written Statement of Work in place for this position from VERSANT's Talent Acquisition team will be deemed the sole property of VERSANT. No fee will be paid in the event the candidate is hired by VERSANT as a result of the referral or through other means.*
Salary Context
This $110K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Versant, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($142K) sits 21% below the category median. Disclosed range: $110K to $175K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Versant AI Hiring
Versant has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Englewood Cliffs, NJ, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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