Cohere

Cohere occupies a specific and important position in the AI landscape: they're an enterprise-focused AI company that builds foundation models for business, without the consumer product distraction of OpenAI or the safety-research emphasis of Anthropic. Founded by Aidan Gomez (co-author of the original "Attention Is All You Need" transformer paper), Ivan Zhang, and Nick Frosst (Geoffrey Hinton lab alumnus), the founding team has some of the deepest academic credentials in the field.

The thesis at Cohere is that enterprises need AI models built specifically for their requirements — data privacy, on-premises deployment, retrieval-augmented generation, fine-tuning, and controllability — and that the consumer-facing labs aren't optimally positioned to serve that. That thesis is playing out.

Who they're hiring

Cohere hires across research, engineering, and go-to-market. Key areas:

• Research — model training, RLHF, multilingual models, retrieval systems (RAG is a core product area), and efficiency research
• Platform engineering — the infrastructure to train, serve, and deploy models at scale; the API platform
• Solutions engineering / ML engineering — helping enterprise customers actually deploy Cohere models in their workflows
• Sales and customer success — a growing function as the enterprise business scales; technical account management and enterprise sales
• Product — product management for the Command model family, the API, and enterprise products

The company is headquartered in Toronto, with offices in San Francisco, London, and New York. Meaningful presence in all four.

The process

The process is thorough and moves at a reasonable pace. For technical roles:

1. Initial conversation — typically with a technical recruiter or team member, covering background and fit
2. Technical screen — varies by role: coding for engineers, research paper discussion or ML concepts for researchers, a take-home or case for product/GTM roles
3. Technical deep dive — usually a longer conversation with a senior team member going into your background and domain knowledge
4. Team interviews — 3-4 conversations covering technical depth, cross-functional fit, and culture
5. Offer

For research roles: Cohere takes the research interview seriously. Expect in-depth discussion of your prior work, your views on key problems in NLP and foundation models, and potentially a presentation of your research.

For enterprise-facing roles (solutions engineering, account executives): the interviews test your ability to engage with complex customer architectures, understand the AI deployment context, and communicate technical concepts to non-technical stakeholders.

What the culture is actually like

Cohere has a strong academic heritage that shows in the culture. The research team treats the work rigorously, the founding team is technically deep, and there's genuine respect for intellectual contribution. It doesn't feel like a pure startup-hustle culture; it feels like a place where people are serious about the science.

At the same time, Cohere is a commercial company with a real revenue mandate. There's a pragmatic orientation alongside the research culture — the models need to work for enterprise customers in real deployments, not just benchmark well. The balance between research depth and customer-facing pragmatism is a real part of the culture.

The company is growing but still has the feel of a place where you can have a real impact. The team is smaller than OpenAI or Google, which means individual contributions are more visible.

Toronto (the main HQ) has a strong tech and AI research community — Hinton is from here, the Vector Institute is here, and there's a meaningful ML talent pool. If you're in Toronto or open to moving, the local context is a genuine asset.

What they look for

NLP / foundation model depth. More than generalist ML. Cohere builds language models specifically, and they hire people who think deeply about language, retrieval, generation, and the specific challenges of making models useful in enterprise contexts.

Enterprise product thinking. Unlike consumer AI companies, Cohere thinks carefully about deployment requirements: data privacy, on-prem vs. cloud, fine-tuning on private data, latency requirements. Engineers and researchers who can engage with that complexity are valued.

Communication. Cohere operates with significant remote work and cross-office collaboration. Clear writing and the ability to work asynchronously matter.

Genuine research credibility (for research roles). The founding team has serious academic credentials, and research hires are expected to match that bar. Publication record or equivalent demonstrable depth is expected.

The RAG and enterprise AI angle

Cohere has made Retrieval-Augmented Generation a core product focus. Command R and Command R+ were built specifically for RAG workflows — grounded responses, citation, long-context retrieval. This is increasingly important in enterprise AI, where you can't just dump proprietary data into a training run.

If you work in RAG, retrieval systems, or applied NLP for enterprise — Cohere is one of the few companies building at this specific intersection at a frontier level.

Things worth knowing

The deployment flexibility is a real differentiator. Cohere can deploy on-premises, in private cloud, and across multiple cloud providers — something OpenAI and Anthropic don't offer at the same level. For enterprise sales and solutions engineering roles, this is a significant selling point.

The competition is fierce. Cohere competes with OpenAI API, Anthropic API, Google Vertex AI, and AWS Bedrock for enterprise customers. The differentiation is real — deployment flexibility, fine-tuning, multilingual — but the competition is well-funded and aggressive. The go-to-market motion requires nuance.

Aidan Gomez is young and technically credible. He's in his late 20s, was part of the transformer paper team at Google Brain, and is genuinely respected in the ML research community. The founding team's credentials are unusual and matter for how the company is perceived and what kind of research it can attract.

The multilingual investment is real. Cohere has invested significantly in multilingual capabilities, which matters for global enterprise customers. Aya, their multilingual initiative, is one of the largest open-source multilingual dataset projects. If you work in multilingual NLP or care about language diversity in AI, this is a distinctive angle.

Toronto headquarters. Most of the technical leadership and research team is in Toronto. San Francisco has a significant presence, but if you're in the Bay Area and want full presence at the center of the research culture, you'd be more remote than at a SF-first company.

Should you apply?

Cohere is a strong choice if you want to work on foundation models in an enterprise context, care about research depth, and are open to the Toronto connection. The founding team is credible, the market position is real, and the enterprise AI opportunity is large. For people who want to be at the frontier of how businesses actually deploy and use AI — rather than at the frontier of building AGI — Cohere is one of the most interesting companies in the field.