Senior Product Engineer

We’re seeking a Senior Product Engineer to build high-impact product features end-to-end — and to leverage AI to scale your own impact. You’ll work across discovery, implementation, and iteration: turning ambiguous problems into shipped outcomes, using LLMs and classic ML pragmatically (where they help), and instrumenting everything for quality, cost, and engagement. This is a hands-on role for a builder who codes daily, partners tightly with Product/Design/Platform, and uses AI tools (generation, retrieval, agents, automation) to move faster with higher quality.

What You’ll Do

• Ship product features end-to-end across backend, APIs, and UI (Python/TypeScript/React or equivalent).
• Use AI as a force multiplier: speed up specs, scaffolding, tests, refactors, data exploration, incident triage, and docs with modern AI tools.
• Add AI-powered user value when it helps: search & RAG, summarization/tagging, recommendations, assistants/automation — always measured against baselines.
• Instrument everything: define KPIs (quality, latency, cost, engagement), set up offline/online evals, A/B tests, and telemetry.
• Build reliable services with CI/CD, canaries/rollbacks, observability (metrics/logs/traces), and clear SLOs.
• Own data flows for features: ETL for embeddings/indices, feedback loops from clicks/dwell/ratings, guardrails for safety and privacy.
• Collaborate with Product/Design on discovery, UX, and roadmap; writes clear specs that feed into how the system is built, tested and documented (context engineering) and drive good decisions quickly.
• Ensure security/IAM and governance (PII handling, RBAC, auditability) in everything you ship.

• 7–10+ years in product/software engineering with a record of shipping user-facing features at scale.
• Strong Python; comfortable across API + web app stacks (FastAPI/Flask/Node, React).
• Demonstrated pragmatic use of AI in products and workflow: prompt/adapter tuning, retrieval, evals, and cost/latency trade-offs.
• Solid engineering fundamentals: system design, testing, code quality, performance, and operational ownership in cloud environments.
• Experience with instrumentation, A/B testing, and analytics to guide iteration.
• Security mindset (privacy, RBAC/IAM, auditability).

• Knowledge graphs or personalization/recommendation experience.
• Agentic workflows (tool use, routing) with safety guardrails.
• ML/LLM observability, eval frameworks, or GPU inference optimization basics.
• Contributions to open-source or internal frameworks that improve developer velocity.