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About This Role
Purpose of the Role
The Associate Manager, Search \& LLM for Personal Care at Unilever plays a critical role in executing and optimizing strategies that drive AI\-powered brand discovery. This position supports the evolution of search across traditional, conversational, and generative interfaces, helping ensure Unilever’s PC brands are discoverable in the moments that matter.
Working closely with the Search \& LLM Manager, this role focuses on the day\-to\-day execution of Answer Engine Optimization (AEO) and holistic search initiatives. The Associate Manager will collaborate across brand, media, content, and agency teams to implement strategies, analyze performance, and continuously optimize brand visibility across AI\-driven platforms.
This is a high\-growth role at the forefront of AI search transformation, offering exposure to cutting\-edge technologies including LLMs, AI overviews, and social search ecosystems.
Key Responsibilities
- LLM Tracking, Reporting \& Intelligence
o Support tracking and reporting of AEO KPIs such as Share off Voice (SOV), brand sentiment, and citation share.
o Maintain and evolve prompt tracking frameworks, “must\-win” queries, and competitive benchmarking analyses
o Conduct ongoing performance analysis and identify optimization opportunities
- Brand.com Content (Owned Ecosystem Optimization)
o Support development of search\-optimized content strategies for brand websites, focusing on answer\-first formats (FAQs, expert content, educational pages)
o Partner with content, UX, brand teams, R\&D, Legal, and Consumer Insights to optimize blog like content, PDPs, and campaign landing pages for AI\-driven discoverability
- Authority Building \& Backlink Strategy
o Partner with PR, content, and agency teams to identify opportunities for high\-quality backlinks and third\-party coverage
o Contribute to development of content and partnerships that strengthen brand presence in sources leveraged by LLMs
- Social Search (Platform Discoverability)
o Support optimization of content for discoverability across social platforms including TikTok, YouTube, and Reddit
o Partner with social, influencer, and agency teams to integrate search insights into content creation and distribution
o Help ensure alignment between social content and broader AEO and search strategies
- Retail.com Content (Commerce \& Conversion Surfaces)
o Partner with retail teams, eCommerce, and brand teams to track and optimize existing PDP content for emerging onsite LLM surfaces like Alexa\+ and Sparky
o Educate retail teams on best in class practices in agentic commerce
- SEM (Paid Search)
o Support execution and optimization of existing paid search campaigns (text, visual Demand Gen and Msan, Pmax), working with brand and agency partners to create approved text and visual copy.
o Monitor campaign performance, analyze key metrics (e.g., ROI, conversion, incremental growth), and surface actionable recommendations
o Pioneer new ad formats (LLM Ads), ensuring scale, efficiency, and brand safety/trust guidelines.
Success Measures
- Holistic search strategy implemented across traditional search, AEO, and LLM, optimizing for zero\-click experiences.
- Unilever PC brands consistently achieve disproportionate Share of Model (SOM) in relevant LLM outputs across major AI platforms.
- Full utilization of AEO performance frameworks, including education, content creation, strategy, and technical optimization.
- Strong authority building and backlink profile, with accurate representation in third\-party content shaping AI responses.
- Delivery of high\-performing, answer\-first content formats optimized for AI discoverability.
Key Skills \& Experience
- BA/BS in Marketing, Digital Media, Communications, or related field
- 3–5\+ years of experience in digital marketing, search (SEO/SEM), content strategy, or analytics
- Analytical mindset with experience working with performance data and dashboards
- Curiosity and passion for emerging technologies, particularly AI and generative search
- Experience with LLM tools, prompt testing, or AI\-driven search platforms (e.g., ChatGPT, Gemini, Copilot)
- Familiarity with content optimization principles including E\-E\-A\-T and structured data
- Exposure to social search strategies and platform\-specific optimization
- Experience working with cross\-functional teams and agency partners within a large, matrixed organization and global brand environment
Pay: The pay range for this position is $88,600 to $133,000\. Unilever takes into consideration a wide range of factors that are utilized in making compensation decisions including, but not limited to, skill sets, experience and training, licensure and certifications, qualifications and education, and other business and organizational needs.
Bonus: This position is bonus eligible.
Benefits: Unilever employees are eligible to participate in our benefits plan. Should the employee choose to participate, they can choose from a range of benefits to include, but is not limited to, health insurance (including prescription drug, dental, and vision coverage), retirement savings benefits, life insurance and disability benefits, parental leave, sick leave, paid vacation and holidays, as well as access to numerous voluntary benefits. Any coverages for health insurance and retirement benefits will be in accordance with the terms and conditions of the applicable plans and associated governing plan documents.
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At Unilever, inclusion is at the heart of everything we do. We welcome applicants from all walks of life and are committed to creating an environment where everyone can thrive/succeed. All applicants will receive fair and respectful consideration, and we actively support the growth and development of every employee.
Unilever is an Equal Opportunity Employer/Protected Veterans/Persons with Disabilities.
For more information on your federal rights, please see Know Your Rights: Workplace Discrimination is Illegal
Employment is subject to verification of pre\-screening tests, which may include drug screening, background check, credit check and DMV check.
If you are an individual with a disability in need of assistance at any time during our recruitment process, please contact us at NA.Accommodations@unilever.com. Please note: This email is reserved for individuals with disabilities in need of assistance and is not a means of inquiry about positions or application statuses. The Protected Veterans or Individuals with Disabilities AAP narratives are available for inspection by any employee or applicant for employment Monday through Friday during normal business hours at establishment.
Salary Context
This $88K-$133K range is in the lower quartile 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 Unilever, 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($110K) sits 49% below the category median. Disclosed range: $88K to $133K.
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.
Unilever AI Hiring
Unilever has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hoboken, NJ, US. Compensation range: $133K - $133K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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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