Cohere

Enterprise AI Platform — Founded by Transformer Co-Author

The consensus on Cohere: Choose Cohere if you want Transformer co-author pedigree and enterprise AI focus — but expect turbulence and lower ratings.

2019
Founded
Toronto
Headquarters
~500
Employees
~$7B
Valuation
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Culture Overview

What it's really like to work at Cohere

Cohere is building enterprise AI tools powered by large language models, founded by Aidan Gomez, a co-author of the original Transformer paper. The culture emphasizes learning and research, with an engineering-driven approach where team members wear many hats in a fast-moving startup environment.

Continuous Learning Engineering-Driven
Glassdoor Ratings

Cohere Glassdoor Ratings & Employee Reviews

2.9
Overall Glassdoor Score
Based on 120 reviews
Culture & Values 3.0
Work-Life Balance 2.5
Compensation & Benefits 3.2
Career Opportunities 2.5
Senior Management 2.3
Overall Rating 2.9
48% Recommend to a Friend
55% CEO Approval (Aidan Gomez)
Employee Review Themes

Working at Cohere: Pros & Cons

✓ What employees love

  • ✓ Cutting-edge NLP research from Transformer co-author
  • ✓ Enterprise AI focus gives real customer impact
  • ✓ Toronto HQ means strong Canadian talent pool
  • ✓ Fast-moving with startup energy

✗ What could be better

  • ✗ Frequent organizational restructuring has led to notable team turnover
  • ✗ Internal communication from leadership could be more transparent and consistent
  • ✗ Career growth paths not well-defined
  • ✗ Engineering processes still maturing
Engineering Culture

How the engineering team works

Tech Stack

Python Go

Products

Command Embed Rerank

Research-to-Product

Engineering team works across the full research-to-product pipeline, turning cutting-edge NLP research into production-grade enterprise APIs.

Team Structure

Small, fast-moving teams with high ownership. Engineers wear many hats across research and production.

Open Positions

Join the Cohere team

... open positions.

Community Sentiment

What the community says about Cohere

Mixed Sentiment 5 mentions from Hacker News & Reddit · Updated 2026-04-21
“I made a small RAG database just using Postgres. I outlined it in the blog post below. I use it for RSS Feed organisation, and searching. They are small blobs. I do the labeling using a pseudo-KNN algorithm.https://aws.amazon.”
Hacker News podgietaru · 2026-01-15 View thread →
“I'd push back on a couple things here.The notion that Scale AI's data is of secondary value to Wang seems wrong: data-labeling in the era of agentic RL is more sophisticated than the pejorative view of outsourcing mechanical turk work at slave wages to third world...”
Hacker News vagabund · 2025-06-28 View thread →
“I did the traditional blog post about this, and used it to create an RSS Aggregator website using AWS Bedrock.https://aws.amazon.com/blogs/machine-learning/use-language-e...”
Hacker News podgietaru · 2025-05-29 View thread →
“I built an rss aggregator with semantic search using embeddings. The main usage was being able to categorise based on any randomly created category. So you could have arbitrary categorieshttps://github.com/aws-samples/rss-aggregator-using-cohere-e...”
Hacker News podgietaru · 2025-05-12 View thread →

Sentiment data refreshed daily from public Hacker News and Reddit discussions. See all company profiles →

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