Staff Software Engineer I (Open)

Thomson Reuters•Frisco, TX

2d•$118,400 - $219,800•Hybrid

About The Position

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.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
8+ years of software engineering experience with demonstrated progression to staff-level or equivalent technical leadership — including ownership of a functional area and leadership of significant cross-team initiatives
Mastery of Python and strong fundamentals across API design, testing strategy (unit, integration, load and evaluation), system design, and security architecture
Track record of designing and operating API-first systems consumed by multiple teams, with a product mindset toward developer experience — including versioning, backward compatibility, and clear contracts
Working fluency with information-retrieval concepts (precision/recall trade-offs, hybrid search, ranking, citation networks) sufficient to make sound architectural decisions and partner credibly with research scientists
Demonstrated experience designing or owning evaluation frameworks for retrieval or LLM systems: dataset design, IR metrics, CI-gated eval suites, and tracing and observability for non-deterministic workflows
Proven reliability ownership at the system level: designing on-call models, setting SLOs, leading incident response, and a clear philosophy that shipping frequently and operating reliably are complementary, not in tension
Demonstrated security and access-control practice:
Fluency with AI-assisted development tools; you write the majority of your code with AI assistance, produce higher-quality work because of it, and establish the patterns that help the team do the same
Strong communication: you explain complex retrieval and architecture concepts to technical and non-technical audiences and build alignment across engineering, product, and science

Nice To Haves

Experience building tools or APIs for agentic AI systems, including tool protocols such as MCP (Model Context Protocol) or similar
Background in legal technology, knowledge management, or other high-stakes information domains where source attribution, accuracy, and entitlement enforcement are critical
Hands-on experience operating search and retrieval engines (OpenSearch, Vespa, Elasticsearch) — cluster sizing, index lifecycle, and the trade-offs between semantic and structured search
Experience operationalizing tooling for LLM-powered workflows: tool-description design, prompt engineering, and agent invocation patterns
Experience establishing contribution governance for a platform built on by multiple teams — review workflows, registry/namespace management, and deprecation and migration policies
Familiarity with content licensing and policy-based entitlements in multi-tenant environments
Working understanding of infrastructure-as-code (Terraform) and API gateways (such as Apigee) — enough to operate, secure, and reason about the services you own

Responsibilities

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
Own verified spec-driven development as a team-wide standard: define how acceptance criteria and evaluation datasets are written before implementation to specify a tool’s behavior, IR metrics (precision, recall, NDCG), and CI gates that verify every change and set the bar every engineer’s eval suite follows
Treat evaluation as foundational infrastructure the team is still maturing: assess current coverage across tool families, close the highest-risk gaps first, and prevent teams from redundantly reinventing evaluation capability
Define how offline grading pipelines and online signals combine into a continuous, trustworthy quality picture for every tool in production
Take full operational responsibility for what you and your area own — you built it, you own it, you run it: own the on-call model and SLO targets for your services, and build the automation that reduces operational toil for the whole team
Lead incident response for high-severity issues, write thorough post-mortems, and drive systematic fixes that prevent recurrence
Build the observability that makes reliability visible — including trace-level instrumentation for agentic retrieval paths, where standard request/response logging cannot explain a non-deterministic failure
Own performance and cost strategy for the tool layer: capacity planning, caching, and rate limiting that keep the platform reliable and economical as tenants, content volumes, and query traffic grow, with fast, safe delivery treated as a first-class engineering goal rather than a trade-off against reliability
Function as the technical authority other engineers and partner teams turn to for Content Tools API, evaluation, and architecture decisions; author cross-team Architecture Decision Records for cross-cutting choices and contribute to the code- and design-review policy the team follows
Multiply the team: develop Senior engineers through architecture guidance, design pairing, and constructive review, with measurable growth in the people you mentor — your impact is the patterns you set and the engineers you grow
Build durable relationships with the teams that depend on you — CoCounsel Engineering, TR Labs, and product owners — , feeding structured requirements back into the roadmap, and reducing time-to-value for new adopters
Establish team-wide AI-assisted development patterns and model them visibly; capture decisions in writing where the team can find them, so work is legible to async readers
Make the hard calls: say no to unnecessary complexity, advise senior leadership on complex , and champion ethical AI and responsible, secure deployment across the team’s work

Benefits

Hybrid Work Model
Flexibility & Work-Life Balance
Career Development and Growth
Industry Competitive Benefits
Culture
Social Impact
Making a Real-World Impact
flexible vacation
two company-wide Mental Health Days off
access to the Headspace app
retirement savings
tuition reimbursement
employee incentive programs
resources for mental, physical, and financial wellbeing
market competitive health, dental, vision, disability, and life insurance programs
competitive 401k plan with company match
parental leave
sabbatical leave
optional hospital, accident and sickness insurance paid 100% by the employee
optional life and AD&D insurance paid 100% by the employee
Flexible Spending and Health Savings Accounts
fitness reimbursement
access to Employee Assistance Program
Group Legal Identity Theft Protection benefit paid 100% by employee
access to 529 Plan
commuter benefits
Adoption & Surrogacy Assistance
Tuition Reimbursement
access to Employee Stock Purchase Plan