layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Applied AI Engineer at Embedding VC, San Francisco, CA

Introduce AI Coding tooling best practices — Bring the engineering depth of Claude Code, Cursor, Devin, Replit Agent, and similar tools (Skills, hooks, subagents, long-context engineering, MCP integration) into ByLabs. Produce engineer onboarding playbooks and advanced-usage standards.
Set the bar for the Skill Marketplace — Define Skill design conventions; personally deliver 5–10 reference-quality Skills as exemplars; raise team-wide Skill engineering quality through PR review.
Prototype an AI-native engineering workflow — Reimagine the spec → design → implementation → review → testing → release → operations lifecycle around AI tools, producing a reference workflow APAC teams can adopt.
Build internal AI application prototypes (employee-facing scenarios) — In OpenClaw, A2UI, role-bound AI assistants, and similar internal contexts, build small frontier prototypes that validate streaming UX, transparent agent steps, long-context memory, multi-agent collaboration, and other emerging patterns.
Provide reference architecture code — When APAC teams face hard architectural choices in new AI scenarios, deliver runnable "this is how Silicon Valley does it" reference implementations — not documents, but working code.
Cross-team craft propagation — One internal tech talk per month, one in-depth blog per quarter, and one onsite workshop in APAC per half year. Distill Silicon Valley AI application best practices into durable team assets.
Architecture review participation — As the AI Team's anchor in the U.S., participate in ByLab's AI architecture review process and provide frontier perspective and taste-based feedback at the application layer.
Major Requirements
Education: Top-tier CS / EE BS or above; MS preferred.
6+ years of software engineering experience, including at least 2 years focused on LLM applications.
Senior AI application engineering experience: Has shipped AI applications from 0 → 1 in production (not demos). Must have at least one tour of duty as a core engineer at a top-tier Silicon Valley AI company or a leading AI product team — examples include Anthropic, OpenAI, Meta, and similar.
Deep AI Coding tooling fluency: Claude Code, Cursor, Devin, or Replit Agent (at least one) is a daily-driver in your workflow, and you can articulate its engineering implementation. Strong understanding of the engineering tradeoffs in Skills, hooks, subagents, MCP, and long-context engineering.
Agent application engineering depth: Solid grasp of ReAct, Plan-Execute, Reflection, Multi-Agent, and Orchestrator-Worker tradeoffs. Hands-on experience with LangGraph, AutoGen, CrewAI, OpenAI Swarm, or in-house frameworks.
Strong full-stack engineering: Strong backend (Go / Python / TypeScript). Mid-level or above frontend (React / Next.js). Comfortable with end-to-end streaming rendering and interaction.
Strong product sense and craft: Comfortable collaborating with PMs and designers; you have considered opinions and taste on AI application UX patterns (streaming, undo, context surfacing, agent-step progress, etc.).
Public technical influence: A track record of public output — blog, talks, OSS contributions — sufficient to represent the team's craft externally.
Self-direction and async collaboration: Capable of independently judging value priorities and driving prototype-to-dogfooding loops; effective at collaborating with APAC counterparts via documents, PRs, and asynchronous communication.
Nice to have
Open-source contributions to LangChain, LlamaIndex, AutoGen, CrewAI, Vercel AI SDK, Claude Code MCP, OpenTelemetry GenAI, or similar projects.
Track record of translating research output (papers, blog posts) into production application patterns.
SaaS B2B product 0 → 1 experience.
Experience productizing multilingual (English / Chinese) applications.
AI experience in the financial or crypto domain.

Applied AI Engineer

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