Staff Software Engineer, Agentic Analytics

Cohere · United States; San Francisco; Canada; New York; Ottawa; Toronto; Montreal

  • Senior
  • Full-time
  • $250,000 – $325,000
  • Posted 2026-11-09
  • Confirmed live on 10 November 2026

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Job description

WHO ARE WE?

Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.

We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.

We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.

We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!

WHY THIS ROLE?

We're building the foundational infrastructure that will define how the world thinks about and deploys AI, and we want the sharpest, most driven, and most curious people to help us do it. As the founding lead of Agentic Analytics, a new team in our Analytics and Data Insights organization, you'll own how Cohere gets answers from its own data: an agentic analytics system that answers business questions over governed data, shows the source, query, and freshness behind every answer, and asks for clarification or declines when the evidence is insufficient.

Agentic analytics is far from a solved problem, and correctness is the hard part. A query that runs can still answer the wrong question. Agents go wrong when a business term maps to more than one table, when data goes stale, and when the right context never reaches the model. The worst failures are silent and plausible: a join that double-counts, a wrong denominator, missing data read as zero. Getting this right takes governed definitions, context an agent can use, and an evaluation bar that decides what ships.

You'll build on work already in motion: an internal analytics agent in pilot on our BigQuery warehouse, which is fed by tested ingestion pipelines and modeled in dbt. In your first year, you'll take it to production for teams across Cohere, one domain at a time, and make it replicable: each new domain stands up with configuration, a fresh evaluation set, and very little new code.

This is a hands-on staff engineering role reporting to our Senior Director of Data Analytics. You'll write production code and evaluation sets yourself while managing a small founding team that grows as the system proves itself. You may have held the title software engineer, AI engineer, or data scientist; what matters is that you've built a system like this and run it in production.

RESPONSIBILITIES

As the lead of Agentic Analytics, you will:

- Own the system end to end. Set the architecture for how a business question becomes a governed query and a verifiable answer: interpreting the question, retrieving the right context, applying approved definitions and joins, enforcing the asking user's permissions, and attaching the evidence (source, query, freshness) to every answer.

- Build and run it in production. Design the core services and write much of the first version yourself: agent-facing data tools (MCP or equivalent), context retrieval, agent skills for analytics, and the APIs that serve answers. Hold them to production standards for reliability, latency, cost, security, and observability. Put it where people already work: Slack, North, IDEs, and other agents.

- Hold every release to the evaluation bar. Build and run the evaluation harness, and define what correct means in each domain: ground-truth question sets, including held-out cases and questions the agent should clarify or decline; result-level scoring, correct-or-decline rates, coverage, and error severity; and regression tests on every change to context, skills, or models. Nothing ships below the bar, and the call on when a domain is ready for production is yours.

- Engineer the context layer. Turn metric definitions, approved joins, documentation generated from the code that builds each table, a business glossary, and worked examples into context the agent can use. Every correction becomes a reviewed definition and a regression test.

- Roll it out across Cohere. Bring domains such as marketing, revenue, finance, and product onto the system one at a time, each with named owners, certified definitions, and its own evaluation set. Measure adoption, accuracy, turnaround, and correction effort against how those teams work today.

- Make it replicable. Package the tools, skills, context templates, and evaluations, and document them well enough that other teams at Cohere can run the system without you.

- Hire and lead the team. Bring on the team's first engineers, set the technical bar, review the work, and develop the people doing it.

- Partner across Cohere. Work with our data platform team on the warehouse and semantic models, our mode

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