Tech Lead/Principal Engineer,Creator Agent Algorithm Infrastructure Seattle Regular Job Seattle area,Washington USA,Software Development

Position: Tech Lead / Principal Engineer, Creator Agent Algorithm Infrastructure Seattle Regular

Tech Lead / Principal Engineer, Creator Agent Algorithm Infrastructure

Location:

Seattle

Team:
Technology

Employment Type:

Regular

Job Code: A184994A

Responsibilities

Lead the architectural development of core Agent algorithm capabilities, including but not limited to:

Agent orchestration framework:
Build agent orchestration capabilities supporting complex business logic, based on Lang Graph or in‑house frameworks.
Agentic Search:
Build intelligent retrieval architecture tailored to creator scenarios, enabling the Agent to proactively and iteratively gather information from product, creator, and content corpora.
Hierarchical memory systems:
Design short-term, long-term, and episodic memory mechanisms, providing the algorithm team with foundational capabilities for personalized creator understanding.
Algorithm tuning infrastructure:
Provide efficient training, evaluation, and iteration infrastructure for Agent RL, Memory RL, SFT, and additional frontier optimization directions.

Continuously track and bring frontier Agent optimization directions into the team, including but not limited to:

Test‑time / inference‑time optimization (self‑refine, reflection, tree search, process reward model–guided reasoning, etc.)
Tool use optimization (tool‑use SFT, tool‑use trajectory RL, tool selection optimization)
Multi‑agent collaboration and deliberation
Automated prompt / workflow optimization (e.g., DSPy, Text Grad — "gradient‑style" optimization of prompts and workflows)
Agent distillation into smaller, more efficient models
Agent evaluation and reward modeling (LLM‑as‑Judge, PRMs, Agent benchmark design, etc.)

Judge which directions are worth investing in and translate them into team capabilities based on team and business realities.

Track the latest Agent architectures from OpenAI, Anthropic, and others, and adapt them deeply to our creator business.

Partner deeply with the Algorithm team — ensures that algorithm infrastructure accelerates rather than bottlenecks algorithmic innovation.

Develop deep understanding of creators as a B2B user group and translate business insights into algorithm infrastructure decisions.

Qualifications

Minimum Qualifications

Deep understanding of the Agent technical stack — familiarity with the architectural approaches of frontier Agent practices such as OpenAI SDK and Claude Code, and a clear point of view on the capability boundaries and algorithmic challenges of Agents.

Hands‑on experience with Lang Graph (or equivalent frameworks) for building production‑grade, domain‑customized agents.

Systematic understanding of Agent optimization — familiarity with the foundational directions (Agent RL, hierarchical memory and Memory RL, SFT), plus hands‑on practice or deep familiarity with at least 2‑3 frontier directions (e.g., test‑time optimization, tool‑use optimization, multi‑agent collaboration, automated prompt/workflow optimization, Agent distillation, Agent evaluation).

Familiarity with Agentic Search design and implementation, with a clear understanding of the paradigm shift from traditional retrieval to agent‑driven retrieval.

Deep understanding of B2B / ToB businesses — able to reason about algorithm infrastructure from B2B‑specific angles (user workflows, ROI, explainability, cont rollability) rather than directly applying consumer‑product intuitions.

Technical judgment and forward‑looking perspective — able to identify directions with real business value in a rapidly evolving Agent landscape, and bring fresh thinking to the team.

Outstanding cross‑team collaboration skills — able to build a deep partnership with the product, algorithm and other cooperation engineer teams and drive complex algorithm projects to high‑quality delivery.

Preferred Qualifications

End‑to‑end experience building LLM‑powered (especially Agent) algorithm infrastructure from 0 to 1.

Deep hands‑on experience with RL / RLHF / DPO / GRPO and other LLM alignment techniques.

Hands‑on exploration or published work in test‑time scaling, process reward models, or Agent self‑improvement.

Practical experience with automated prompt / workflow optimization (DSPy, Text Grad, etc.).

Experience with algorithm infrastructure for content…

Tech Lead​/Principal Engineer, Creator Agent Algorithm Infrastructure Seattle Regular

Tech Lead/Principal Engineer, Creator Agent Algorithm Infrastructure Seattle Regular