Interested in this AI/ML Engineer role at Merkle, Inc.?
Apply Now →Skills & Technologies
About This Role
Digital Performance
Chicago - N. State
About the job
As a Manager, SEO & AI Discovery at Merkle, you will lead SEO strategy across a portfolio of client accounts, setting program direction, owning complex audits and recommendations, and translating search performance into business outcomes. This role is built for a senior individual contributor who can move comfortably between technical SEO, content and on-page strategy, AI-driven discovery, performance measurement, and client advisory. You will serve as a trusted lead for assigned accounts, helping clients understand what is happening, why it matters, and what to do next.
Core Responsibilities
- Lead SEO strategy across assigned accounts: Set search program direction, prioritize workstreams, and ensure recommendations connect to client business goals, marketing priorities, and measurable outcomes.
- Serve as the day-to-day search lead: Build trusted relationships with client stakeholders, understand their goals, KPIs, business context, priorities, and decision-making dynamics, and translate search priorities into clear, business-relevant action.
- Own complex SEO / AI audits and analyses: Independently diagnose performance opportunities across technical SEO, content and on-page optimization, AI Search visibility, competitive landscape, and site architecture.
- Develop measurement frameworks: Define SEO reporting structures that connect organic visibility, technical health, content performance, classic SERP visibility, AI Overview presence, LLM visibility, and business outcomes.
- Turn data into decisions: Analyze data from GSC, GA4, crawl tools, rank tracking, log files, AI visibility platforms, and third-party tools to identify patterns, explain performance shifts, and prioritize action.
- Create actionable recommendations: Translate findings into prioritized recommendations with clear impact, effort, dependencies, confidence level, and implementation guidance for client, content, and engineering teams.
- Lead technical SEO workstreams: Guide recommendations across crawl, indexation, rendering, JavaScript SEO, Core Web Vitals, site architecture, internal linking, schema, structured data, migrations, and re-platforming.
- Advance content and AI Search visibility: Lead search intent mapping, topical authority planning, SERP feature analysis, AI Overview evaluation, LLM visibility analysis, entity and brand authority recommendations, and scalable on-page optimization frameworks.
- Own delivery quality: Manage assigned SEO workstreams end-to-end, including scoping, work planning, QA, stakeholder updates, timeline management, and on-time delivery.
- Mentor and review junior work: Coach consultants and staff consultants on analysis planning, data interpretation, recommendation quality, deliverable structure, and client-ready communication.
- Support account growth: Identify organic growth opportunities, contribute SEO expertise to pitches, RFPs, renewals, and SOW scoping, and help shape work that is clearly scoped, valuable to clients, and feasible for the team to deliver.
- Improve the SEO practice: Contribute to internal templates, playbooks, QA standards, training materials, AI-assisted workflows, and process improvements that help the team deliver better work at scale.
What You’ll Bring
- 3+ years of progressive SEO experience, preferably in an agency, consultancy, or complex multi-stakeholder environment.
- Strong technical SEO capability across crawl, indexation, rendering, site architecture, internal linking, Core Web Vitals, structured data, migrations, and JavaScript SEO.
- Demonstrated capability in SEO analysis and measurement, including the ability to combine multiple data sources and translate findings into business-relevant recommendations.
- Hands-on experience with tools such as Google Search Console, GA4, Screaming Frog, Sitebulb, Ahrefs, SEMRush, enterprise crawlers, rank tracking platforms, Data Studio, and AI visibility tracking platforms.
- Ability to build clear, persuasive client deliverables, including audits, executive summaries, dashboards, roadmaps, and strategic recommendation decks.
- Strong written, verbal, and presentation skills, with the ability to adjust communication for executives, marketers, engineers, content teams, and internal stakeholders.
- Experience leading client conversations, answering questions in real time, and explaining and supporting recommendations with evidence, context, and judgment.
- Working knowledge of AI Search, including AI Overviews, LLM-powered discovery, citation visibility, entity authority, and AI-assisted SEO workflows.
- Strong project and workstream ownership, including the ability to manage timelines, flag risks early, coordinate with PMs and cross-functional teams, and keep stakeholders informed.
- Comfort mentoring consultants and staff consultants and reviewing their work for analytical accuracy, strategic clarity, and client readiness.
Preferred / Bonus Experience
- BigQuery, SQL, Python, server log analysis, API-based reporting, or other scalable data workflows.
- Experience with international SEO, e-commerce SEO, local SEO, video/image SEO, link building, digital PR, or SEO testing.
- Experience developing reusable SEO templates, processes, playbooks, training materials, or productized service offerings.
Leadership & Ways of Working
- You are proactive, responsive, and self-directed. You readily provide status/account updates, risks, or next steps.
- You can manage multiple accounts or workstreams at once without losing quality, context, or follow-through.
- You are comfortable with ambiguity and can turn broad client questions into structured analysis plans and practical recommendations.
- You raise risks early, propose paths forward, and balance client needs with team capacity and scope boundaries.
- You help make the team better by sharing what you know, improving how work gets done, and creating space for consultants and staff consultants to grow.
The annual salary range for this position is 65K- 75K. Placement within the salary range is based on a variety of factors, including relevant experience, knowledge, skills, and other factors permitted by law.
Benefits available with this position include:
Medical, vision, and dental insurance,
Life insurance,
Short-term and long-term disability insurance,
401k,
Flexible paid time off,
At least 15 paid holidays per year,
Paid sick and safe leave, and
Paid parental leave.
Dentsu also complies with applicable state and local laws regarding employee leave benefits, including, but not limited to providing time off pursuant to the Colorado Healthy Families and Workplaces Act, in accordance with its plans and policies. For further details regarding Dentsu benefits, please visit www.dentsubenefitsplus.com.
To begin the application process, please click on the "Apply" button at the top of this job posting. Applications will be reviewed on an ongoing basis, and qualified candidates will be contacted for next steps.
At dentsu, we believe great work happens when we’re connected. Our way of working combines flexibility with in-person collaboration to spark ideas and strengthen our teams. Employees who live within a commutable distance of one of our hub offices, currently located in Chicago, metro Detroit, Los Angeles, and New York City, are required and expected to work from the office three days per week (two days per week for employees based in Los Angeles). Dentsu may designate other Hub offices at any time. Those who live outside a commutable range may be designated as remote, depending on the role and business needs. Regardless of your work location, we expect our employees to be flexible to meet the needs of our Company and clients, which may include attendance in an office.
#LI-EB1 #LI-HYBRID
Salary Context
This $65K-$75K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Merkle, Inc., 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($70K) sits 67% below the category median. Disclosed range: $65K to $75K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Merkle, Inc. AI Hiring
Merkle, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $75K - $75K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% below 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.