Staff Software Engineer I (Open)

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

You’re an ideal fit if you have:

Required Qualifications

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

Preferred Qualifications

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

What Success Looks Like

Develop a comprehensive mental model of the Content Tools architecture — tool inventory, CI/CD pipelines, evaluation infrastructure, and the legacy dependencies and migration paths in flight — and identify the top architectural risks and debt items across the team’s systems by shipping multiple tools

Establish working relationships with CoCounsel Engineering, Product, and TR Labs, understanding each stakeholder’s priorities and integration pain points firsthand

Establish your AI-assisted development workflow and model it visibly for the team; take on-call ownership for your functional area

Ship at least one high-impact improvement that demonstrates judgement and taste, not just velocity, and assess eval coverage across tool families to pinpoint the most urgent gaps

Drive the architecture for a major Content Tools initiative — a significant catalog expansion, the evaluation framework, or a comparable platform milestone — from design through production, establishing the patterns and templates other engineers build against

Establish Content Tools as a well-documented, measurably reliable internal platform: verified spec-driven development as the default, SLO coverage across core services, automated eval gates in CI, on-call runbooks, and a track record of fast, high-quality incident resolution

Multiply the team: at least two Senior engineers have grown measurably through your architecture guidance, design pairing, and mentorship

Be recognized across the organization as the Content Tools architecture authority — shaping team standards, influencing cross-team architecture, and advising leadership on complex

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