Dev G YouTube channel thumbnail
Dev G
Subscribers 35.8K
Videos 158
Views 1.1M

Channels Like Dev G

Dev G covers guidance for landing ML and software engineering roles, with a focus on new grad paths, ML engineer career tips, resume optimization, and interview prep. Content appears as tutorials and strategy guidance, often addressing job search tactics, portfolio projects, and FAANG-related pathways. The channel publishes new content regularly, with recent videos spanning roughly 5–10 minutes and average views around 564 per video among 158 published pieces, totaling 1.1M views for 35.8K subscribers from the United States since November 2023.

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Similar Channels

We found 48 YouTube channels similar to Dev G

Egor Howell YouTube channel thumbnail
#1

92% relevance

Subscribers 67.7K
Videos 104
Views 3.0M
Appearances 40
SERP 100%
Similarity 87%
ML engineer job tips ml projects portfolio resume for ML engineers

Both target ML engineers with tips for landing roles and building portfolios; they share high search overlap on 'ML engineer job tips' and 'ml projects portfolio,' though Egor's content stacking is slightly more focused on practical job tips (92% match via 100% search, 87% content).

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Marina Wyss - AI & Machine Learning YouTube channel thumbnail
#2

72% relevance

Subscribers 128K
Videos 88
Views 3.8M
Appearances 34
SERP 54%
Similarity 83%
ml projects portfolio resume for ML engineers land ML offers

Similar audience seeking ML opportunities and portfolios, with high similarity on 'ml projects portfolio' and 'land ML offers,' but Marina's content approach spends more on broad AI/ML career guidance (72% match; search 54%, content 83%).

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Zen van Riel YouTube channel thumbnail
#3

66% relevance

Subscribers 29K
Videos 85
Views 1.3M
Appearances 15
SERP 47%
Similarity 79%
ML engineer job tips resume for ML engineers land ML offers

Shares focus on ML engineer job tips and resume/offer land strategies; strong audience overlap for 'ML engineer job tips' and 'land ML offers,' though Zen’s content style is a bit more resume-focused (66% match; search 47%, content 79%).

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Tech With Tim YouTube channel thumbnail
Subscribers 2.0M
Videos 1.8K
Views 209.2M
Appearances 24
SERP 37%
Similarity 80%
SWE interview prep ML engineer job tips FAANG interview prep

Appeals to aspiring engineers with interview prep alongside ML job tips and FAANG prep signals; considerable search overlap on 'SWE interview prep' and 'ML engineer job tips' but Dev G’s content tilts more toward ML-specific career paths (63% match; search 37%, content 80%).

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Boris Meinardus YouTube channel thumbnail
#5
Boris Meinardus

60% relevance

Subscribers 96.9K
Videos 79
Views 4.0M
Appearances 7
SERP 23%
Similarity 84%
ML engineer job tips resume for ML engineers new grad ML roles

Targets ML engineers with job tips and resume guidance, including new grad roles; shares audience via 'ML engineer job tips' and 'resume for ML engineers,' yet Boris emphasizes fresh-graduate pathways (60% match; search 23%, content 84%).

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Maddy Zhang YouTube channel thumbnail
#6

54% relevance

Subscribers 58.6K
Videos 338
Views 6.4M
Appearances 10
SERP 13%
Similarity 82%
ml projects portfolio resume for ML engineers senior vs new grad ML

Both channels discuss ML projects portfolios and resumes; high audience overlap on portfolio/search topics, though Maddy’s content leans toward senior vs. new grad perspectives (54% match; search 13%, content 82%).

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Content Landscape

Top competitors by match strength are Egor Howell (92% match) and Marina Wyss - AI & Machine Learning (72% match). They share overlapping queries with Dev G such as ML engineer job tips, ML projects portfolio, and resume for ML engineers. Zen van Riel (66% match) also overlaps on ML engineer tips and ML project/resume topics. Tech With Tim (63% match) overlaps on SWE interview prep and FAANG-related topics, while Boris Meinardus (60% match) covers ML engineer tips and new grad ML roles. Dev G has 35.8K subscribers, while these competitors range from about 29K to 2M subscribers, with Tech With Tim being the largest. Shared queries include ML projects portfolio, resume for ML engineers, and land ML offers, indicating converging search intent around landing ML/software roles and preparation for FAANG-style interviews.

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Video Highlights

Recent content from similar channels

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Search Queries Used

new grad software engineer SWE interview prep ML engineer job tips FAANG interview prep Bay Area tech jobs ml projects portfolio resume for ML engineers land ML offers AI ML job search tech job interview strategies machine learning career path new grad ML roles sWE job application tips senior vs new grad ML portfolio projects ML software engineer interview tips data scientist career path machine learning jobs tips entry level ML roles resume tips ML engineers portfolio ML projects SQL for engineers coding interview practice system design basics algorithm interview prep tech interview strategies Bay Area tech jobs tips remote software engineer jobs SWE interview guide ML engineer resume sample career path ML engineer new grad software roles senior vs junior ML machine learning portfolio ideas AI engineer job search

Frequently Asked Questions

Which YouTube channels are most similar to Dev G?

Egor Howell — 67.7K subscribers (92% match), Marina Wyss - AI & Machine Learning — 128K subscribers (72% match), Zen van Riel — 29K subscribers (66% match). All three focus on AI, machine learning, and tech career guidance, similar to Dev G's emphasis on ML/AI job paths and new grad opportunities.

What type of content does Dev G make?

Dev G creates content about machine learning and software engineering career guidance, focusing on landing AI/ML and SWE roles for new grads. Uploads appear to be multiple videos, with recent titles ranging from 179 to 1.3K views, averaging about 564 views per video.

How do we determine which channels are similar to Dev G?

We analyze Dev G's recent videos, generate topic-relevant search queries, check YouTube search results, and compare the meaning of each channel's content to measure similarity. The result is a ranked list sorted by SERP overlap, semantic similarity, and search appearances.

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