Interested in this AI/ML Engineer role at The Nielsen Company?
Apply Now →Skills & Technologies
About This Role
Company Description
At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.
Job Description
This opportunity is for Nielsen’s Gracenote division - the premier entertainment metadata company in the world, powering the search, discovery, and navigation systems for the globe's biggest tech, automotive, and media giants.
As entertainment becomes more fragmented and AI reshapes the way people discover and engage with content, trusted data and technology are powering the future and are the foundation for better experiences. In partnership with our clients and by fully embracing AI, we are reinventing the business, products and teams to capture a once-in-a-lifetime opportunity which will significantly impact the media and entertainment ecosystem as a whole.
We are seeking a highly technical, customer-focused Product Solutions Architect (PSA) to lead technical pre-sales and implementation for our suite of API and AI products, with a primary focus on creating agents that solve customer problems with our newly launched and forthcoming Gracenote Model Context Protocol (MCP) Servers. You will serve as the technical bridge between our customers and our product/engineering teams, enabling B2B clients to successfully integrate our authoritative metadata into their LLM-powered discovery experiences, and highlight the value of AI-first approaches to their business problems.
Key Responsibilities
- Technical Pre-Sales & Consultation: Partner with Sales to apply Gracenote’s GTM strategy, demonstrate the value of AI solutions to a variety of customer stakeholders (Product, Technology, or Marketing -oriented profiles). Lead discovery sessions, understand customer use-cases, AI -based architecture and identify tangible value propositions for how Gracenote metadata can improve customer’s discovery experience using AI. Elaborate and run proof of concepts. Own and secure customer engagement and adoption through contract signature.
- Develop Agentic Solutions: Design, build, and maintain a portfolio of focused, single-purpose AI agents that demonstrate specific technical capabilities of the Gracenote Video MCP Server — translating complex, multi-step entertainment workflows (such as conversational search, catalog enrichment, and personalized recommendations) into tangible, hands-on prototypes
- Deploy on Frameworks: Possess experience deploying these agents on various agentic frameworks, such as those provided by Google or AWS, to prove their efficacy
- Solution Architecture: Analyze complex, siloed customer environments to recommend optimal integration patterns and map Gracenote tools/resources to customer-specific AI -based workflows. Act as "Customer Zero" to familiarize with, test and validate new MCP tools and features before they roll out to clients.
- Implementation Excellence: Guide developers through the technical adoption process, providing code-level guidance, documenting integration use cases, and helping customers leverage JSON-RPC 2.0 standards for seamless API/server interaction.
- Product Advocacy: Function as the "voice of the customer" to influence product roadmaps, translating customer requirements into actionable product inquiries (PIs) and technical documentation.
- Strategic Engagement: Establish standardized SLAs and best practices for implementation, ensuring consistent delivery across Tier 1 and Tier 2 global accounts.
Qualifications* Technical Mastery: Solid grasp of agent creation and agentic deployment frameworks and MCP tooling, specifically how LLMs utilize MCP primitives (Tools, Resources, Prompts), JSON-RPC 2.0, and AWS ecosystem with specific, practical experience leveraging AWS Cognito for identity management, user authentication, and secure API access control.
- Agentic AI & Orchestration: Experience designing, deploying, or observing multi-step AI agents using one or more of the following frameworks: Google ADK, AWS Bedrock Agents, LangChain, or LlamaIndex preferred. Deep understanding of tool chaining (MCP), system prompt engineering, function calling across major LLMs (Gemini, Claude, GPT), and step-level tracing or response evaluation.
- Consultative Expertise: Proven ability to navigate complex enterprise security (OAuth/OIDC) and data privacy requirements while effectively communicating technical solutions to diverse audiences, from C-level executives to engineering teams.
- Experience: Strong experience in Python or TypeScript/Node.js to support PoCs and integration with B2B enterprise APIs. Experience connecting MCP clients and servers to managed enterprise foundation models via AWS Bedrock, Google Cloud Vertex AI, Microsoft Azure AI / Foundry, etc. Familiarity with large-scale structured datasets, content discovery architectures, and search & recommendations engine mechanics. Familiarity with the CTV/Streaming and the entertainment ecosystem is extremely welcome but not required.
- Customer interaction skills: Proven ability to bridge the gap between complex LLM/MCP architectures and business objectives, leveraging strong consultative leadership and stakeholder management skills to guide discovery, execute Proof of Concepts (PoCs), and effectively communicate technical strategy to audiences ranging from C-suite executives to engineering teams.
- Travel: Ability to travel to customer sites globally up to 40% of the time to conduct workshops, discovery sessions, and on-site technical support.
#LI-AW1
Additional Information Enabling your best to power a better media future.
Holistic Rewards: We are committed to an inclusive benefits package that supports our employees and their families. This includes comprehensive health and wellness plans, a 401(k) with a Nielsen company match, and a generous paid time off policy. Depending on the role, additional benefits may include a company-provided vehicle and/or discretionary incentive/bonus eligibility.
Compensation Transparency: The posted base salary range is a reasonable estimate that may be adjusted based on the final work location of the selected employee. Individual pay within the range is determined by factors such as experience, training, geography, certifications, and business needs. Beyond base salary, this role may be eligible for bonuses, equity, or other incentives.
Nielsen makes hiring decisions without regard to disability status, protected veteran status, or membership in any other protected class.
Please be aware that job-seekers may be at risk of targeting by scammers seeking personal data or money. Nielsen recruiters will only contact you through official job boards, LinkedIn, or email with a nielsen.com domain. Be cautious of any outreach claiming to be from Nielsen via other messaging platforms or personal email addresses. Always verify that email communications come from an @nielsen.com address. If you're unsure about the authenticity of a job offer or communication, please contact Nielsen directly through our official website or verified social media channels.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At The Nielsen Company, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
The Nielsen Company AI Hiring
The Nielsen Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.