Staff Software Engineer

This role is for a staff engineer who independently leads the design, development, and delivery of complex, high-impact AI-native and cloud-native systems. The IC4 engineer takes ownership of critical components or features, sets technical direction within their domain, and serves as a technical resource to peers and junior engineers. They combine deep software engineering fundamentals with hands-on expertise in AI/ML integration, cloud architecture, and production systems. This role requires the ability to balance technical depth with organizational impact, working across teams to solve ambiguous problems and drive platform improvements. Responsibility Area: Technical Leadership & Architecture • Lead the design and architecture of complex, production-critical AI-native systems, including LLM integrations, agentic workflows, retrieval pipelines, and inference services • Own end-to-end delivery of medium-to-large features or systems, including specification, design, implementation, testing, deployment, and production monitoring • Make informed architectural and technical trade-off decisions between latency, cost, scalability, reliability, and model quality • Establish evaluation frameworks, testing strategies, and production monitoring for AI-driven features; drive adoption of eval-driven development practices • Design and optimize high-performance data pipelines, embedding systems, and vector stores handling millions of rows and complex retrieval scenarios • Champion security best practices, including prompt-injection defense, data privacy, PII handling, responsible AI guardrails, and compliance with regulatory requirements • Participate in architectural reviews across teams and contribute to platform-level technical decisions • Stay current with emerging AI/ML frameworks, services, and techniques; evaluate and recommend adoption where applicable Responsibility Area: Mentorship & Technical Influence • Mentor junior and mid-level engineers (IC2/IC3) through pair programming, design reviews, architecture discussions, and hands-on coaching • Conduct thorough code reviews, including evaluation of prompts, agent logic, retrieval strategies, and AI/model behavior validation • Identify technical debt and improvement opportunities; advocate for and lead initiatives to address them • Share knowledge through documentation, brown-bag sessions, and architectural guidance • Contribute to hiring and interview processes; provide technical assessment and feedback on engineering candidates • Model best practices in software craftsmanship, testing discipline, and debugging methodology • Support onboarding of new team members and ensure knowledge transfer across projects Responsibility Area: Cross-Functional Collaboration & Communication • Work closely with product management, domain specialists, and other engineering teams to define requirements and technical specifications • Translate complex technical concepts and AI trade-offs for non-technical stakeholders (product, leadership, customers) • Lead root cause analysis for customer-reported issues, including those involving model behavior, hallucinations, retrieval quality, and distributed system complexity • Participate in design reviews, technical planning sessions, and retrospectives; contribute perspectives on technical feasibility and risk • Communicate clearly about design decisions, implementation challenges, AI limitations, and production incidents • Build and maintain relationships with adjacent teams and external partners where applicable • Proactively identify opportunities to improve development processes, tooling, and team efficiency Responsibility Area: Software Development Excellence • Develop robust, maintainable, and well-tested AI-native software using Python, Java, and JavaScript as appropriate • Build comprehensive unit tests, integration tests, and AI evaluation harnesses (accuracy, regression, hallucination detection) • Troubleshoot complex issues in distributed systems, p