How to Apply to Cohere

Key Takeaways

Tailor your resume around Cohere's specific product stack (Command, Embed, Rerank, RAG) and use their terminology naturally — this signals domain readiness and improves Ashby keyword matching
Before applying, read at least three recent Cohere research papers or blog posts and reference specific insights in your cover letter or screening question responses
For technical roles, prepare for system design questions focused on LLM inference infrastructure, distributed training, and enterprise-scale ML deployment — not generic algorithms
Demonstrate your ability to work in a distributed-first environment by citing specific examples of async collaboration, cross-timezone coordination, and documentation-driven workflows
Complete every optional field in Ashby — LinkedIn, GitHub, Google Scholar, portfolio — because Cohere recruiters use these to build a holistic candidate profile before the first call
Research Cohere's competitive positioning against OpenAI, Anthropic, and Google — articulating why enterprise-focused, cloud-agnostic AI matters will distinguish you in culture-fit conversations

About Cohere

Cohere is one of the most closely watched enterprise AI companies in the world, building large language models (LLMs) and natural language processing tools that help businesses integrate generative AI into their products and workflows. Founded in 2019 by Aidan Gomrat, Ivan Zhang, and Nick Frosst — with roots in Google Brain and the University of Toronto's influential machine learning lab — Cohere occupies a distinctive niche: it competes with OpenAI and Anthropic not by chasing consumer hype, but by focusing squarely on enterprise deployment, data privacy, and cloud-agnostic flexibility. Its flagship products include Command (text generation), Embed (semantic search), and Rerank (search relevance), all available via API or deployable on private cloud environments. Cohere's culture blends academic rigor with startup velocity. The team includes researchers who have published at NeurIPS, ICML, and ACL alongside engineers who have scaled production systems at Google, Meta, and Amazon. The company operates with a distributed-first mindset, with employees across Toronto, San Francisco, London, and Seoul, among other locations. This global footprint is reflected in its job postings, which frequently specify regional hubs rather than a single headquarters. People are drawn to Cohere because it sits at the frontier of AI research while shipping real products to real enterprises — a rare combination that lets employees publish papers and push production code in the same quarter. With significant funding rounds from investors including Inovia Capital, NVIDIA, and Salesforce Ventures, Cohere offers the financial stability of a well-capitalized startup alongside the intellectual ambition of a research lab.

Application Process

Identify Your Role and Regional Fit
Cohere posts roles across multiple global hubs — Toronto, San Francisco, London, New York, Seoul, and Bengaluru among others. Many roles specify a region explicitly (e.g., 'Software Engineer, Internal Infrastructure (Europe & UK)' or 'MLE (Korea)'). Before applying, confirm you meet any location or timezone requirements, as Cohere's distributed model still expects regional alignment for many teams.
Submit Your Application Through Ashby
All applications route through Cohere's Ashby-powered careers portal at cohere.com/careers. You'll create a candidate profile, upload your resume, and answer role-specific screening questions. Some technical roles may ask for links to your GitHub, Google Scholar profile, or a portfolio of relevant projects — have these ready before starting.
Recruiter Screen
Successful applicants typically hear from a Cohere recruiter within one to three weeks. This initial call commonly lasts 30 minutes and covers your background, interest in Cohere's mission, and logistical details like location, compensation expectations, and timeline. Expect questions about why enterprise AI and why Cohere specifically — generic enthusiasm for 'AI' won't distinguish you.
Technical or Functional Assessment
For engineering and research roles, Cohere typically administers a technical assessment — this could be a take-home coding challenge, a system design exercise, or a research presentation depending on the seniority and focus of the role. For non-technical roles like Revenue Operations or Marketing Events, expect a case study or work sample that simulates a real Cohere business scenario.
Team Interviews (Virtual On-Site)
The core interview loop commonly involves three to five sessions conducted over video, given Cohere's distributed nature. Engineering candidates should prepare for live coding, system design at scale (think LLM inference infrastructure), and ML-specific problem solving. You'll likely meet your prospective manager, two to three peers, and a cross-functional partner. Culture-fit conversations at Cohere tend to probe collaboration habits and intellectual curiosity more than abstract 'values alignment.'
Hiring Committee and Leadership Review
For senior and staff-level roles, interview feedback commonly goes through a hiring committee or leadership review stage. This is where Cohere calibrates across candidates and ensures alignment on level, scope, and team fit. This stage can add several days to the process, so patience is warranted.
Offer and Onboarding
Offers from Cohere typically include competitive base salary, equity in the form of stock options, and comprehensive benefits. Given Cohere's global footprint, offer structures may vary by region. Onboarding is designed around the distributed model, with structured first-week programs that connect new hires with their team, an onboarding buddy, and key cross-functional stakeholders.

Resume Tips for Cohere

Critical Lead with LLM and NLP-Specific Technical Depth

Cohere's core products are large language models, embedding models, and retrieval-augmented generation (RAG) systems. If you've worked on transformer architectures, fine-tuning, prompt engineering, vector databases, or inference optimization, make these the first things a hiring manager sees. Use specific terminology — 'trained a 7B parameter model on custom enterprise data' is far stronger than 'worked on machine learning projects.' Even for non-ML roles, demonstrating fluency in AI concepts signals you can operate effectively in Cohere's environment.

Critical Quantify Scale and Impact with Precision

Cohere serves enterprises processing millions of API calls. Your resume should speak this language. Instead of 'improved model performance,' write 'reduced inference latency by 40% on a 13B parameter model serving 2M daily requests.' For non-technical roles, quantify similarly: 'managed $1.2M event budget across 15 enterprise conferences' or 'built revenue forecasting pipeline covering 200+ enterprise accounts.' Ashby's structured evaluation makes it easy for reviewers to compare quantified achievements across candidates.

Critical Highlight Research Publications and Open-Source Contributions

Cohere was co-founded by researchers and maintains an active publication record. For research and MLE roles, list your most relevant publications directly on your resume — not buried in a separate section, but prominently featured with venue names (NeurIPS, ICML, ACL, EMNLP). Open-source contributions to projects like Hugging Face Transformers, LangChain, or Cohere's own open-source tools (like Cohere Toolkit) carry significant weight. Link directly to your work.

Mirror Cohere's Product Language in Your Experience

Study Cohere's product pages for Command, Embed, Rerank, and their RAG capabilities. Then mirror that language naturally in your resume. If you've built semantic search pipelines, mention 'embedding-based retrieval' and 'reranking.' If you've worked on text generation systems, reference 'grounded generation' or 'enterprise-grade LLM deployment.' This isn't keyword stuffing — it's demonstrating that you understand Cohere's problem space and can contribute from day one.

Showcase Enterprise and B2B Experience for Go-to-Market Roles

Cohere sells to enterprises, not consumers. For roles like Director of Revenue Operations, Marketing Events Specialist, or Senior Revenue Accountant, foreground your experience with enterprise sales cycles, B2B SaaS metrics (ARR, net retention, pipeline coverage), and complex deal structures. Mention specific tools common in enterprise go-to-market stacks — Salesforce, HubSpot, Clari, Netsuite — as these signal operational readiness for Cohere's revenue infrastructure.

Use Clean, Single-Column Formatting for Ashby Parsing

Ashby handles modern resume formats well, but multi-column layouts, text boxes, and heavy graphics can still cause parsing issues. Stick to a single-column layout with standard section headers (Experience, Education, Skills, Publications). Use a PDF format to preserve formatting. Avoid headers and footers for critical information like your name or contact details, as some parsers skip these regions.

Demonstrate Distributed-Team Collaboration Skills

Cohere operates globally across multiple time zones. Include concrete examples of working effectively in distributed or remote-first environments — asynchronous communication practices, cross-timezone project coordination, or documentation-driven development workflows. A line like 'Led a six-person distributed team across Toronto and London, shipping model evaluation framework using async stand-ups and detailed RFCs' tells Cohere you can thrive in their operating model.

Include a Concise Technical Skills Section Optimized for ATS Filtering

Ashby supports keyword-based candidate filtering. Include a dedicated skills section listing specific technologies, frameworks, and methodologies: Python, PyTorch, JAX, CUDA, Kubernetes, Terraform, Apache Spark, dbt, Airflow, SQL, gRPC, and any cloud platforms (AWS, GCP, Azure) you've used. For research roles, include methodologies like RLHF, DPO, constitutional AI, or mixture of experts. This section ensures you surface in recruiter searches even if your bullet points use different phrasing.

ATS System: Ashby

Ashby is a modern, analytics-forward applicant tracking system favored by high-growth startups and technology companies. It combines ATS, CRM, and scheduling functionality into a single platform, giving Cohere's recruiting team structured scorecards, pipeline analytics, and collaborative evaluation tools. Ashby parses resumes into structured candidate profiles, making clean formatting and relevant keywords essential for visibility.

Submit your resume as a PDF — Ashby parses PDFs reliably and preserves your intended formatting better than Word documents
Use standard section headers like 'Experience,' 'Education,' 'Skills,' and 'Publications' so Ashby correctly categorizes your information
Avoid multi-column layouts, tables, and text boxes — Ashby's parser reads top-to-bottom and may scramble non-linear content
Include role-relevant keywords naturally in your bullet points, not just in a skills section — Ashby supports full-text search across your entire resume
Complete all optional fields in the application form (LinkedIn URL, portfolio, GitHub) as Cohere's recruiters use Ashby's candidate profiles to quickly evaluate fit
Answer screening questions thoroughly — Ashby's structured intake forms let recruiters filter and sort by your responses, making thoughtful answers a differentiator
Keep your resume to two pages maximum — Ashby displays parsed content in a scrollable profile, and overly long resumes dilute the signal recruiters are scanning for

Complete Ashby Resume Guide

Interview Culture

Cohere's interview process reflects its identity as a research-driven enterprise AI company: expect technical rigor paired with genuine intellectual curiosity. The company typically runs a four-to-six-stage process — recruiter screen, technical assessment, and a virtual on-site loop of three to five interviews — though the exact structure varies by role and seniority. For engineering and research roles, technical interviews commonly include live coding in Python, system design focused on ML infrastructure (think: designing an inference serving system for a multi-tenant LLM, or architecting a distributed training pipeline), and deep dives into your past work. Research-focused positions like Member of Technical Staff or Senior Research Engineer often include a research presentation where you walk through a published paper or significant project. Interviewers care less about memorized algorithms and more about how you reason through novel problems — reflecting Cohere's culture of first-principles thinking. For go-to-market and operations roles, expect case-based interviews that simulate real Cohere scenarios. A Revenue Operations candidate might be asked to design a pipeline forecasting framework, while a Marketing Events Specialist could walk through how they'd plan Cohere's presence at a major industry conference like NeurIPS or AWS re:Invent. Culture-fit conversations at Cohere probe for specific traits: intellectual humility (can you say 'I don't know' and then reason toward an answer?), collaborative instincts (how do you give and receive feedback across distributed teams?), and genuine passion for the AI space. Cohere's interviewers are often researchers and senior engineers who have published widely — they'll notice if your enthusiasm for AI is surface-level. Prepare by reading Cohere's recent blog posts, their research papers (especially around Command R and Aya), and understanding how their products differ from competitors like OpenAI and Anthropic. Demonstrating that you understand Cohere's enterprise-first positioning and multilingual research agenda will set you apart from candidates who simply say they're excited about AI.

What Cohere Looks For

Deep technical expertise in LLMs, NLP, or distributed systems — Cohere builds frontier models and expects contributors who can operate at that level
Enterprise mindset — understanding how AI products need to work differently when deployed for regulated industries, private clouds, and security-conscious organizations
Research-to-production fluency — the ability to take a concept from a paper and ship it as a reliable, scalable product feature
Intellectual curiosity and humility — willingness to challenge assumptions, engage with unfamiliar problems, and learn publicly from mistakes
Strong asynchronous communication skills — writing clear documentation, RFCs, and Slack messages that work across time zones
Multilingual and global perspective — Cohere's Aya project and global team mean cultural awareness and multilingual sensitivity are valued, especially in research and product roles
Ownership and initiative — in a startup scaling rapidly, Cohere looks for people who identify problems and drive solutions without waiting for direction
Collaborative disposition — the ability to work effectively across research, engineering, product, and go-to-market teams in a flat, fast-moving organization

Frequently Asked Questions

How long does the Cohere hiring process typically take from application to offer?

Based on common patterns at companies of Cohere's size and stage, the full process typically takes three to six weeks from initial application to offer. The recruiter screen usually happens within one to three weeks of applying, followed by a technical assessment and virtual on-site loop spaced over one to two weeks. Senior and leadership roles may take longer due to additional calibration and leadership review stages. Cohere's distributed team can sometimes introduce scheduling complexity across time zones, so building in flexibility helps.

Does Cohere require a cover letter with applications?

Cohere's Ashby application forms don't always include a mandatory cover letter field, but when one is available, submitting a strong cover letter is highly recommended. Use it to explain why Cohere's enterprise AI mission resonates with you specifically — not just that you're interested in AI generally. Reference a specific Cohere product, research paper, or company initiative (like the Aya multilingual project) to demonstrate genuine engagement. For technical roles, a cover letter can also contextualize career transitions or highlight projects that your resume alone doesn't fully capture.

What experience level does Cohere typically hire for?

Cohere hires across a wide experience spectrum, from Member of Technical Staff roles (which can include strong early-career researchers with exceptional publication records) to Director and senior leadership positions. The company's sample job titles reveal roles like Software Engineer, Senior Software Engineer, Senior Research Engineer, and Director of Revenue Operations — indicating opportunities at multiple levels. That said, Cohere's interview bar is high across all levels. Even for earlier-career positions, demonstrable expertise in LLMs, NLP, or relevant enterprise technologies is typically expected. Strong academic credentials or open-source contributions can partially compensate for fewer years of industry experience.

Does Cohere support remote work?

Cohere operates with a distributed-first model, with team members across Toronto, San Francisco, New York, London, Seoul, Bengaluru, and other locations. Many roles are listed with specific regional designations (e.g., 'Europe & UK' or 'Korea'), suggesting that while remote work is common, there are often geographic or timezone requirements. Some roles may require proximity to a specific hub for in-person collaboration. Check each job posting's location field carefully and be prepared to discuss your timezone overlap and collaboration preferences during the recruiter screen.

How should I prepare for a technical interview at Cohere?

Preparation should center on three areas: LLM systems knowledge, coding proficiency, and Cohere-specific product understanding. For system design, practice architecting ML serving infrastructure — inference optimization, model sharding, caching strategies for large language models, and multi-tenant API design. For coding rounds, focus on Python with an emphasis on data processing, algorithm implementation, and ML pipeline code rather than pure competitive programming. For research roles, prepare a clear, compelling presentation of your most relevant work. Finally, familiarize yourself with Cohere's technical blog, their approach to RAG, and how their models compare architecturally to competitors. Interviewers will notice when you've done your homework.

What format should my resume be in when applying through Cohere's Ashby portal?

Submit a clean, single-column PDF of no more than two pages. Ashby parses PDFs reliably, and this format preserves your intended layout. Avoid using tables, multi-column designs, graphics-heavy templates, or infographic-style resumes — these can confuse the parser and result in scrambled candidate profiles. Use standard section headers (Experience, Education, Skills, Publications) so Ashby categorizes your information correctly. Place your name and contact information in the body of the document, not in headers or footers, which some parsers skip.

Should I follow up after submitting my Cohere application?

A thoughtful follow-up can help, but timing and channel matter. Wait at least two weeks after submitting your application before reaching out. LinkedIn is often the most effective channel — identify the recruiter or hiring manager for your role and send a brief, personalized message that references the specific position and why you're a strong fit. Avoid generic messages. If you have a mutual connection at Cohere, an internal referral carries significant weight at startups and can move your application to the top of the review queue. Do not follow up more than once unless you receive a response.

What makes Cohere different from other AI companies like OpenAI or Anthropic?

Cohere differentiates primarily through its enterprise-first strategy, cloud-agnostic deployment model, and commitment to data privacy. While OpenAI and Anthropic have focused heavily on consumer-facing products and API access through their own infrastructure, Cohere allows enterprises to deploy models on their own private cloud environments (AWS, GCP, Azure, or on-premises), which is critical for regulated industries like finance and healthcare. Cohere also has a strong multilingual research agenda, exemplified by the Aya project, one of the largest open-science initiatives for multilingual AI. Understanding these distinctions and articulating why they matter is essential for any Cohere interview.

How important are research publications for getting hired at Cohere?

For research-focused roles like Member of Technical Staff and Senior Research Engineer, a strong publication record at top venues (NeurIPS, ICML, ACL, EMNLP, ICLR) is a significant advantage and often an expectation. However, Cohere also values impactful open-source contributions, production ML experience, and demonstrated ability to translate research into shipped products. For engineering roles that are more infrastructure or product-focused (like Software Engineer, Collect or Senior Software Engineer, Model Integration), publications are a bonus but not a requirement — practical experience scaling ML systems matters more. Tailor your emphasis accordingly based on the role.

Sample Open Positions

Sources

Cohere Careers Page — Cohere
Cohere Company Overview and Research — Cohere
Cohere Blog — Product and Technical Updates — Cohere
Ashby ATS — How It Works for Candidates — Ashby
Cohere Glassdoor Reviews and Interview Insights — Glassdoor