Staff Applications Dev Engineer

• A major product or technical subsystem end-to-end, including its architecture, design, implementation, scalability, reliability, security, and ongoing evolution.
• The technical vision and architecture for your area, including key design decisions and interfaces with other systems and teams.
• The quality and business/technical outcomes of your subsystem, with measurable targets for reliability, performance, AI quality, latency, cost, and customer impact.
• The architecture and engineering practices required to build AI-native applications at production scale.
• Technical direction for engineers working within your area, providing guidance through architecture, design reviews, code reviews, and hands-on technical leadership.
• The evolution of AI/ML capabilities such as agentic workflows, retrieval, model integration, evaluation, and intelligent automation within your product area.
• The technical strategy for balancing AI capability, engineering complexity, reliability, security, latency, and cost.

• Take highly ambiguous and complex problems and turn them into clear technical strategies, architectures, and executable plans.
• Own the architecture of major systems or subsystems and drive them from concept through production at scale.
• Make sound technical decisions under uncertainty and clearly articulate architectural trade-offs.
• Define system boundaries, interfaces, APIs, data flows, and integration patterns across multiple services and teams.
• Drive architecture and design reviews and establish a high engineering bar for scalability, reliability, security, maintainability, and performance.
• Identify architectural risks and technical debt and drive long-term improvements across your area.
• Influence technical direction beyond your immediate team through strong technical judgment and collaboration.

• Remain hands-on in building complex software across the stack, from frontend experiences and APIs to backend services, data systems, AI services, and cloud infrastructure.
• Design scalable full-stack architectures using technologies such as React, TypeScript, Python, Java, Go, and modern cloud-native platforms.
• Build distributed services, event-driven systems, APIs, databases, caching, messaging, and scalable data pipelines.
• Ensure systems are observable, resilient, secure, and operationally excellent in production.
• Lead by example through high-quality implementation, testing, debugging, code reviews, and engineering practices.

• Define and drive the adoption of AI-native architectures and engineering patterns across your technical area.
• Design and build production-grade LLM and agentic systems, including: Multi-agent orchestration Tool and function calling Planning and reasoning loops Context and memory management Retrieval and grounding Failure recovery and resilience Human-in-the-loop workflows
• Multi-agent orchestration
• Tool and function calling
• Planning and reasoning loops
• Context and memory management
• Retrieval and grounding
• Failure recovery and resilience
• Human-in-the-loop workflows
• Integrate frontier models from providers such as OpenAI, Anthropic, Google, or equivalent platforms, making informed decisions around model capability, cost, latency, and reliability.
• Design RAG and retrieval systems using embeddings, vector search, hybrid search, semantic retrieval, re-ranking, and enterprise data sources.
• Establish robust AI evaluation strategies and measurable quality metrics for AI-powered functionality.
• Drive AI observability covering model quality, latency, cost, failures, hallucination/error rates, and system behavior.
• Establish appropriate AI safety, security, governance, privacy, and guardrail mechanisms for production systems.
• Evaluate emerging AI capabilities and determine how and where they can create meaningful product or engineering value.

• Provide technical direction and mentorship to engineers across the workstream.
• Lead complex engineering initiatives through influence rather than organizational authority.
• Mentor senior and emerging engineers on architecture, system design, full-stack development, and production AI practices.
• Partner closely with product, design, platform, data, security, and other engineering organizations to translate customer problems into scalable technical solutions.
• Facilitate technical alignment across teams and resolve architectural disagreements through data, experimentation, and sound engineering judgment.
• Establish reusable patterns, frameworks, libraries, and engineering practices that improve productivity across teams.
• Help define the organization's approach to AI-native software development and AI-assisted engineering.

• Champion effective use of AI coding agents and development tools such as Claude Code, Codex, Cursor, Windsurf, or equivalent technologies.
• Establish engineering practices for using AI to accelerate development while maintaining code quality, security, testing, and accountability.
• Identify opportunities to use AI across the software development lifecycle, including design, implementation, testing, debugging, documentation, and code review.
• Share learnings and establish best practices that enable teams to become more effective AI-native engineering organizations.

• A strong track record of owning significant software systems or subsystems end-to-end in production.
• Deep full-stack engineering expertise with the ability to work across frontend, backend, APIs, data, AI services, and cloud infrastructure.
• Strong understanding of distributed systems, system architecture, data structures, algorithms, APIs, databases, scalability, reliability, and cloud-native development.
• Expert-level programming experience in Python, Java, Go, TypeScript, or equivalent languages.
• Hands-on experience designing and delivering AI/ML-powered production systems.
• Strong practical knowledge of modern AI technologies including LLMs, RAG, embeddings, vector search, agentic workflows, tool calling, model evaluation, and AI observability.
• Experience taking ambiguous problems from concept and experimentation through reliable production systemsthat other engineers or product

• 8+ years of software engineering experience, or equivalent practical experience.
• Experience designing and delivering production software systems.
• Experience building or integrating AI/ML-powered applications in a production or near-production environment.
• Modern AI experience: LLMs, RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability.
• Strong programming experience in Python and/or Java, Go, or a similar language.
• Cloud-native technologies, distributed systems, APIs, databases, and scalable architectures.
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience.
• Cybersecurity or security-product experience is a plus.

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