Principal Engineer in Technical Fellow Office

Technical Fellow Office

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.

• 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.