Staff Software Engineer

About this role

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Skills you'll use

What you'll do

• Convert product requirements, business goals, and ambiguous problem statements into structured, testable specifications — with clear scope, interfaces, constraints, non-goals, and acceptance criteria — that both engineers and AI agents can act on with high accuracy.
• Drive technical design and architecture for Workplace Service Delivery, ensuring scalability, performance, and reliability across multi-tenant enterprise deployments.
• Define the data models, composable building blocks, and integration contracts that underpin employee self-service, request management, and WSD×CSM/FSM workflows.
• Decompose work into agent-sized tasks and direct AI coding agents to generate, modify, and refactor production code across multiple files and services — supervising several workstreams in parallel.
• Author and maintain the context that drives correct results: system prompts, agent configuration files, architectural decision records, glossaries, golden examples, and agent-readable documentation.
• Determine when to delegate to an agent versus implement directly, making sound sequencing decisions before work begins and course-correcting quickly when output diverges from intent.
• Design and maintain the constraints that make agent output trustworthy at scale: automated tests (including property-based and mutation tests), evaluation suites, CI/CD quality gates, and least-privilege execution environments.
• Rigorously evaluate code — human- or agent-generated — for correctness, spec adherence, security, performance, and maintainability, confirming behaviour matches intent and that edge cases and failure modes are genuinely handled.

Who can apply

• Build measurable evaluation frameworks — benchmarks, golden datasets, continuous evaluation pipelines, and drift detection — that hold production AI to the same rigour as deterministically testable software.
• Take end-to-end responsibility for the software you ship: integrate changes safely, monitor production, and feed production signals back into specs and evaluation sets.
• Defend against risks specific to AI-integrated systems: prompt injection, sensitive-data and secret leakage, tool-access governance, and model abuse — translated into concrete engineering controls.
• Monitor production AI for hallucinations, behavioural drift, and safety regressions; maintain agent observability through reasoning traces and model quality telemetry; implement rollback mechanisms when autonomous behaviour deviates from intent.
• Treat the development process itself as a product: trace failures back to missing specs or context gaps, refine prompts, tests, and guardrails, and raise the team's overall throughput and reliability — not just your own.
• Mentor and grow lead engineers and senior engineers, investing in their AI-native fluency, architectural judgment, and verification rigour.
• Collaborate with product managers and UX designers to translate requirements and mockups into fully functional, accessible, and delightful employee experiences.
• 10+ years of experience designing and building scalable, reusable enterprise software products and components.

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