AI Native Staff Engineer

# AI Native Staff Engineer

You're an experienced software architect who has built agentic AI systems and LLM-powered features in production. You're not experimenting—you've done this and know what works at scale.

## What you'll do

Build AI-Native Product Systems

• Design and ship AI-powered product features end-to-end
• Architect systems where LLMs, agents and traditional software work together
• Implement RAG pipelines, structured reasoning flows and tool-using agents
• Continuously improve reliability, latency and cost-efficiency

Engineer with AI at full capacity

• Use AI agents to accelerate development, testing and architecture decisions
• Ship rapid prototypes and production-grade systems
• Set up internal AI tooling that multiplies team output
• Push the boundaries of current models

Own your impact

• Translate product problems into scalable AI-powered solutions
• Measure real-world performance (accuracy, business impact, UX)
• Optimize for production robustness over demo quality
• Shape AI-native engineering culture

## What we're looking for

• 8+ years of professional software engineering experience
• Proven track record in designing and shipping large-scale production systems
• Architectural ownership of services or product domains
• Strong backend engineering fundamentals (APIs, distributed systems, data modeling, concurrency, reliability)
• Experience operating production systems (monitoring, incident handling, performance tuning)
• Cloud-native experience (GCP, AWS, Azure)
• Shipped LLM-powered features in production to real users
• Designed and implemented RAG systems in production environments
• Built or architected AI agents or multi-step reasoning systems
• Designed evaluation frameworks for LLM output quality, safety and regression detection
• Optimized AI systems for latency, cost-efficiency and reliability
• Integrated vector databases and embedding pipelines into scalable systems
• Actively used AI tools and agents to augment engineering workflows
• Designed systems where AI components and deterministic systems work together
• Converted rapid AI prototypes into production-grade systems
• Strong intuition around when to use AI vs deterministic logic
• Led complex technical initiatives end-to-end
• Translated ambiguous business problems into system architecture
• Mentored senior engineers or set technical standards
• Shipped high-impact features with measurable outcomes

We're rethinking recruitment with AI as the core of the product, not an add-on.