Software Engineer, AI Engineer (Applied​/Software), Backend Developer

The CoCounsel Agent Context team is seeking a Staff Software Engineer to set the technical direction for Content Tools — the purpose-built APIs that give AI agents precise, governed access to Thomson Reuters’ proprietary legal content across various international jurisdictions. Content Tools are the bridge between TR’s authoritative content and the AI products, such as CoCounsel Legal, that depend on them, and they carry high stakes — every result an agent returns must be accurate and grounded in TR’s authoritative content.

This Staff Engineer defines how those tools are designed, evaluated, and operated, and sets the patterns that every engineer — on this team and on contributing partner teams — builds against. This is a high-leverage role at the intersection of API design, information retrieval, and agentic systems. Staff Engineers here define what to build and why, not just how: they solve ambiguous problems that span multiple teams and systems, and their work is felt well beyond their own code.

The team’s operating principles are non-negotiable. You own your work end-to-end, from the first commit to the production dashboard. You ship to production constantly, and you treat delivery friction as an engineering problem to solve, not a fact of life. You use AI-assisted development as a primary tool, with the majority of code written with AI assistance. And you practice verified spec-driven development: acceptance criteria and an evaluation suite define a tool’s behavior before the implementation exists and gate every change in CI.

Because AI writes most of the code, what you truly own is the spec and the eval that verifies it — the eval is the spec, and you are accountable for the outcome, not the lines. The successful candidate brings platform thinking, deep API and evaluation instincts, enterprise-grade security awareness, and a product-minded approach to developer experience — treating each tool’s contract as a product in its own right.

• Content Tool Architecture & API Ownership Set the technical direction for the Content Tools catalog: define the design patterns, schema templates, and contribution standards every tool is built against, and the guidance on when to create a new tool versus extend an existing one — resolving problems of broad scope with little precedent to guide solutions. Drive an API-first, contract-driven approach: every tool ships with a versioned contract, schema, and ownership metadata that makes it discoverable and self-service for consuming teams and agent developers, who should never need to ask you how to use your tool.
  
  Architect the retrieval and search layer behind the catalog — relevance and ranking quality on the team’s Vespa based search platform, and the trade-offs between structured and semantic retrieval — partnering with TR Labs on the underlying search algorithms. Define the agent invocation model for the catalog: clean tool boundaries exposed over MCP (Model Context Protocol), result payloads designed for token economy, and latency budgets that hold across multi-hop retrieval chains, giving agent orchestrators predictable, well-documented interfaces to TR and third-party content.
  
  Build and own the shared development and testing infrastructure — common libraries, scaffolding generators, mock services, and local environments, built on the team’s Python, FastAPI, and Claude Agent SDK stack — that makes it easy for any team member to build a Content Tool correctly the first time.
• Verified Spec-Driven Development & Evaluation Own verified spec-driven development as a team-wide standard: define how acceptance criteria and evaluation datasets are written before implementation to specify a…