Cohere is the enterprise-focused LLM company — emphasizing private deployment, retrieval-augmented generation, and multilingual models (Aya, Command R/R+). Founded by ex-Google Brain researchers including Aidan Gomez. Late-stage. The interview emphasizes LLM systems, RAG infrastructure, and the engineering of enterprise-grade inference.
Process
Recruiter screen → 60-minute coding phone (DSA medium-hard) → onsite virtual: 2 coding, 1 ML system design, 1 craft deep-dive, 1 behavioral. ML/Research candidates also get a research deep-dive. Cycle: 4–6 weeks.
What they actually ask
- Design a retrieval pipeline for enterprise documents (chunking, embedding, reranking)
- Design a multi-tenant inference platform with token-level billing
- Design a fine-tuning service for customer-private models
- Coding: medium-hard DSA, sometimes ML-flavored
- Behavioral: research-engineering collaboration, ownership, customer focus
Levels and comp (2026)
- SE: $200K–$270K total (cash + late-stage equity)
- Senior SE: $290K–$390K total
- Staff: $400K–$560K total
- Principal / ML Research: $550K–$900K+ total at top of band
Prep priorities
- Be fluent in Python (research, training) and Go/Rust (serving)
- Understand transformer internals, RAG patterns, and reranking
- Brush up on inference optimization (batching, KV cache, speculative decoding)
Frequently Asked Questions
Is Cohere remote-friendly?
Hubs in Toronto (HQ), London, San Francisco, NYC. Many engineering roles fully remote within Canada/US/UK.
How does Cohere compare to OpenAI or Anthropic?
Cohere is enterprise-and-private-deployment focused, less consumer presence. Comp is below OpenAI/Anthropic at top of band but competitive at junior/mid levels.
What is the engineering culture?
Research-engineering hybrid, calmer than the OpenAI/Anthropic intensity. Strong publication culture.