Staff Software Engineer

Qualifications

• Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
• 9+ years software engineering experience (backend, frontend, or full stack)
• Strong JavaScript/Node.js, React and API integration skills
• Automated testing (unit + integration) and CI/CD pipeline experience
• Solid understanding of data modelling and query optimization
• Agentic delivery experience:
• Hands-on experience with AI/GenAI or ML-driven features (LLM prompting, NLU, classification models, or similar)
• Hands-on experience with LLM-integrated features: prompt design, context injection, output tuning, guardrails.
• Experience with NLU/intent classification or conversational AI systems
• Familiarity with ML-driven automation (classification, clustering, recommendation systems)
• Production AI integration. Experience integrating large language model APIs and retrieval-grounded features, including agent orchestration, tool and function calling, and structured output enforcement.
• Applied machine learning literacy. A working command of the concepts that govern how these systems behave — evaluation, embeddings, and the probabilistic output and failure modes of modern models — sufficient to reason about, debug, and verify model-driven behaviour in production.
• Accountable use of AI coding agents. Current, effective use of AI coding assistants and agents with evidence of accountable delivery: precise specification, critical review of generated output, and verification harnesses.
• Operational experience. Hands-on CI/CD, containerized workloads, and observability experience, plus direct on-call and incident-command experience with customer-facing systems.
• Mentorship. Demonstrated mentorship of less-experienced engineers and a record of raising quality through code review.
• Education. Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent practical experience. Advanced degrees are a plus but not a substitute for a record of shipping reliable AI-native applications.
• HR domain depth. HR case management, the employee lifecycle, or payroll, benefits, leave, and absence, at a depth sufficient to challenge a requirement rather than only implement it — including why the same policy question resolves differently by jurisdiction, employment type, or plan year.
• HCM and downstream integration. Integration with core HR, payroll, and benefits systems of record, including the reconciliation and eventual-consistency problems that come with treating an external system as the authority on employment data.
• Extensible product engineering. Building capability that customers configure, extend, and override on their own instances, where instruction and tool surfaces are versioned contracts rather than internal implementation.
• Evaluation and observability tooling. Evaluation frameworks, prompt and instruction management tooling, tracing for model-driven applications, and analysis of production transcripts at scale, particularly where transcript content is itself access-restricted.
• Conversational channel breadth. Employee self-service portals, virtual agent or chat shells, workplace messaging clients, and voice, including handoff between automated and live agents.
• Conversation design partnership. Working alongside conversation or content designers on dialogue flow, tone, and error-recovery design — with attention to how an HR answer is worded when the subject is the employee's own pay, health coverage, or employment status.
• Forward deployed delivery. Building against a customer's data, integrations, and channels, and tuning instructions and evaluation sets in their environment.
• Now Platform depth. Scoped applications, ACLs and platform security rules, Flow Designer, UI Builder, Automated Test Framework, and upgrade-safe extension patterns.