Applied AI Engineer

Embedding VC; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Embedding VC in San Francisco seeks an Applied AI Engineer to anchor our US AI team, leading tooling and application exploration across internal fintech contexts.

You will ship reference implementations, drive streaming UX patterns, long-context memory, and multi-agent coordination while mentoring others and shaping architecture.

Qualifications

• Education: CS/EE BS or higher; MS preferred.
• 6+ years software engineering experience, with at least 2 years focused on LLM applications.
• Senior AI application engineering experience: shipped AI apps from 0 → 1 in production (not demos).
• Experience with at least one: Claude Code, Cursor, Devin, or Replit Agent.
• Strong backend (Go / Python / TypeScript) and frontend (React / Next.js).
• Public technical influence: blog posts, talks, OSS contributions.

Responsibilities

• Introduce AI Coding tooling best practices — bring tools like Claude Code, Cursor, Devin, Replit Agent into ByLabs.
• Set the bar for the Skill Marketplace with 5–10 exemplar Skills.
• Prototype an AI-native engineering workflow and reference processes for APAC teams.
• Build internal AI application prototypes in OpenClaw, A2UI, and role-bound assistants.
• Provide reference architecture code for APAC teams facing hard architectural choices.
• Cross-team craft propagation via tech talks, blogs, and onsite workshops.
• Participate in AI architecture reviews for ByLab.

Job description

About the Role

We are hiring a Applied AI Engineer for a leading fintech company's AI Team to serve as the team's technical anchor in the United States.

You will lead two primary streams:

• AI Coding tooling and developer experience — bringing the engineering depth of Silicon Valley front-line teams (Claude Code, Cursor, Devin, Replit Agent, etc.) into ByLabs.
• AI application paradigm exploration and dogfooding — building small, frontier prototypes in internal scenarios that validate streaming UX, transparent agent steps, long-context memory, and other emerging patterns, and propagating them across the team through reference code, blog posts, and workshops.

This role is about craft and taste. You are not a platform architect, nor a BU project delivery engineer. You are the person who turns front-line Silicon Valley product engineering experience into multiplicative leverage for the entire AI Team.

Responsibilities

• 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.