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Youtube Consultant Jobs (NOW HIRING)

Google Inc.

... consulting, business strategy, investment banking, venture capital, or private equity role ... We make YouTube successful by shaping people's perceptions of YouTube and inspiring them to engage ...

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How much do youtube consultant jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for youtube consultant in the United States is $41.55, according to ZipRecruiter salary data. Most workers in this role earn between $29.33 and $49.52 per hour, depending on experience, location, and employer.

What does a YouTube consultant do?

A YouTube Consultant helps creators and businesses grow their presence on YouTube by providing strategic advice, optimizing content, and improving channel performance. They analyze audience engagement, SEO, monetization opportunities, and video production quality. Their goal is to increase views, subscribers, and overall channel success through data-driven strategies and best practices.

What are the key skills and qualifications needed to thrive as a YouTube consultant?

To thrive as a Youtube Consultant, you need expertise in digital marketing, content strategy, video production, and SEO, often backed by experience in social media management or digital branding. Familiarity with analytics platforms like YouTube Analytics, TubeBuddy, and certifications in areas like Google Ads or YouTube Content Ownership can be highly beneficial. Outstanding communication, creative problem-solving, and adaptability are crucial soft skills for working with diverse clients and evolving trends. These abilities ensure consultants can effectively grow YouTube channels, engage audiences, and deliver measurable results for clients and brands.

What are some typical challenges YouTube consultants face, and how can they address them?

Youtube Consultants often encounter challenges such as staying updated with frequent algorithm changes, addressing audience engagement drops, and helping clients stand out in a highly competitive landscape. Success in this role requires continuous learning, data-driven experimentation, and proactive communication with clients to adjust strategies quickly. Building strong collaboration with content creators and maintaining flexibility in approach are also critical. By staying ahead of industry trends and leveraging analytics, consultants can effectively troubleshoot issues and guide clients toward consistent channel growth.

How to become a YouTube consultant and get paid?

A YouTube consultant helps creators optimize their channels and content strategies. To become one, develop expertise in YouTube analytics, SEO, and content creation, and build a portfolio or network to attract clients. Consultants typically charge hourly or project-based fees and may work independently or through agencies.

What is a YouTube consultant?

A YouTube consultant is a professional who advises content creators and businesses on optimizing their YouTube channels, improving video quality, increasing audience engagement, and growing subscribers. They often have expertise in video marketing, analytics tools, and SEO strategies to enhance channel performance.
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Youtube Consultant jobs near you
Infographic showing various Youtube Consultant job openings in the United States as of September 2026, with employment types broken down into 1% Locum Tenens, 1% As Needed, 90% Full Time, 6% Part Time, and 2% Contract. Highlights an 68% Physical, 3% Hybrid, and 29% Remote job distribution, with an average salary of $86,430 per year, or $41.6 per hour.

Senior Forward Deployed Engineer, GenAI, YouTube GTM Operations

Los Angeles, CA • On-site

Worky
See more jobs at Workyexternal

$114K - $155K/yr

Other

Posted 26 days ago

Key responsibilities

  • Build and iterate on GenAI proof-of-concepts to demonstrate feasibility and translate business problems into software.

  • Lead delivery of complex, production-grade AI solutions from rapid prototypes to maximize business ROI.

  • Author technical designs, write clean code, build intuitive frontends, define metrics, and execute deployment.

Job description

Minimum qualifications:
  • Bachelor's degree in Computer Science, Electrical Engineering, Mathematics or related quantitative field, or equivalent practical experience in software development.
  • 6 years of experience in full-stack software development and system design.
  • Experience with front-end languages (e.g., JavaScript or TypeScript).
  • Experience with back-end languages (e.g., Java, Python, Go or C++) and building applied AI solutions/agentic workflows around pre-trained models.
  • Experience working with database technologies (e.g., SQL, NoSQL), distributed systems, and designing back-end data pipelines.
Preferred qualifications:
  • 2 years of experience as a Technical Lead or Engineering Manager, including zero-to-one delivery and scoping in ambiguous environments.
  • 2 years of experience with Site Reliability Engineering, Information Security, or Developer Operation practices, with practical enterprise AI experience (LLM evals, observability, safety).
  • Experience building advanced GenAI (multi-step LLM, multi-agent systems, or MCP integrated with orchestration frameworks).
  • Experience with AI data infrastructure: vector databases, embedding generation, search architectures, and state-management challenges.
  • Experience integrating tools with enterprise business systems and CRMs.
  • Knowledge of GTM/sales workflows.
About the job:

It's an exciting time to join GTMO's AI Accelerator team, devising the AI transformation for YouTube Business GTM operations. The AI Accelerator is driving AI transformation across the organization. Our mission is to operate as a high-velocity, horizontal transformation engine, partnering directly with YouTube Business GTM business domains to fundamentally redesign legacy workflows from scratch and leverage applied AI to drive direct business impact for YouTube. We operate at the intersection of consulting, product strategy, applied AI and systems engineering. We identify the most painful operational bottlenecks across the organization and rapidly deploy intelligent, enterprise-grade AI powered solutions to solve them.

As an AI Forward Deployed Engineer (FDE) within the YouTube Business organization, you are an entrepreneurial, full-stack builder tasked with influencing the future of YouTube’s Go-To-Market (GTM) operations. Operating as a builder-consultant, you will bridge the critical gap between frontier AI capabilities and production-grade reality. You will actively code, debug, and ship AI powered and agentic solutions that solve complex business problems.

You will deliver scalable solutions that seamlessly connect intuitive frontend user experiences with complex backends and data pipelines. By integrating next-generation AI (LLMs, RAG, and agentic workflows) into GTM operations, you will systematically address the core blockers to enterprise AI maturity, such as integration complexities, data readiness issues, and state-management challenges. If being at the intersection of engineering, GenAI, and product strategy excites you, and you are ready to deliver solutions that have an immediate, outsized impact on YouTube’s business workflows, this is the role for you.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $155000 - $224000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:
  • Build and iterate on GenAI proof-of-concepts (Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic Frameworks) to demonstrate feasibility, translating business problems into software.
  • Lead delivery of complex, production-grade AI solutions (e.g., multi-agent systems, model context protocol servers) from rapid prototypes to maximize business Return on Investment (ROI).
  • Partner with Product Managers and stakeholders to co-create tool roadmaps that enable YouTube's business operations.
  • Author technical designs, write clean code, build intuitive frontends, define metrics, and execute deployment. Build high-performance eval pipelines and observability frameworks to ensure agentic systems meet accuracy, safety, and latency requirements.
  • Resolve technical hurdles preventing AI maturity, including data readiness gaps, system integration complexities, edge cases, and state-management challenges.
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