Principal Engineer

Who you are

• 10+ years of hands-on engineering in software development, system design, or architecture
• Applied AI / agentic engineering: Real experience with modern LLMs and agentic frameworks — prompt/context engineering, tool use, RAG, orchestration, and evaluation — including the sharp edges (latency, cost, reliability, non-determinism, safety)
• Production AI systems: A track record of taking AI/ML-driven features from prototype to production with proper evaluation, monitoring, and guardrails
• Distributed systems & data: Strong background in scalable, fault-tolerant services and in streaming/pipeline technologies (Kafka, Flink, Spark)
• Cross-functional impact: Ability to drive alignment and technical decisions across teams, and to mentor engineers toward better technical decision-making

What the job involves

• Join the Technical Fellow Office to solve hard, ambiguous problems, set the bar for technical excellence, and shape how the broader engineering organization builds — across teams and beyond our immediate department. You will work at the intersection of large-scale distributed systems and the frontier of agentic AI, architecting the platforms, workflows, and guardrails that let autonomous and human-in-the-loop AI systems do real work at scale
• If you want to work where cutting-edge AI meets production-grade engineering — and influence how an entire division builds — this is that role
• Architect & build: Lead the design of critical systems — agentic pipelines, orchestration platforms, and the services around them — running design reviews and contributing hands-on to development
• Ship agentic systems: Productionize LLM-powered workflows — multi-agent orchestration, tool use, retrieval/knowledge systems, and human-in-the-loop control planes that are reliable, observable, and cost-aware
• Define quality for AI: Build the evaluation harnesses, benchmarks, and automated quality gates that measure and enforce quality in non-deterministic systems, and catch regressions in agent and model behavior
• Incubate new ideas: Rapidly prototype POCs that validate strategic bets, especially at the intersection of AI and existing platforms
• Advise & de-risk: Guide complex technical decisions, help teams adopt AI pragmatically, and avoid the failure modes of hype-driven design
• Surface risks: Identify blind spots, technical risks, and inefficiencies — including the novel risks of autonomous systems — and proactively drive solutions
• Set the technical bar: Write exemplary code across the ecosystem, and raise the level of engineering through architectural reviews, mentorship, and knowledge sharing across teams

Benefits

• Comprehensive health, life and disability insurance
• Employee stock ownership
• Generous vacation and personal days
• Office food and free snacks, lots of health options!
• Employee Resource Groups
• Training and development programs
• Commute subsidy
• Competitive retirement/pension plans
• Paid leave for new parents
• Mental Health and Wellbeing programs and support
• Global Employee Assistance Program
• Volunteering and donation matching program