Senior ML Engineer - Agentic AI Systems (Remote, Equity)

Factored; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Ownership through equity participation

Annual company retreat

Education bonus for continuous学习

Job summary

Factored is seeking a Senior Machine Learning Engineer focused on agentic workflows. You will design and deploy systems that convert LLMs into autonomous agents capable of planning, tool-use, and self-correction.

You will architect task decomposition, memory management, and robust interfaces between LLMs and APIs, with guardrails and HITL checks for high-stakes decisions, in a fully remote environment. You will collaborate with data scientists and software engineers across time zones, and

Qualifications

• Bachelor’s or Master’s in CS, statistics, mathematics, or related field.
• 5+ years hands-on experience developing and deploying ML models in production environments.
• Strong fundamentals in data structures, algorithms, system design, OOP, and API design.

Responsibilities

• Architect how the agent breaks down complex user requests into sub-tasks.
• Develop Plan-and-Execute or ReAct patterns for model thinking and action.
• Design memory management for long-running tasks to prevent goal drift.
• Create interfaces between LLMs and external software, databases, or APIs.
• Standardize function calls, legacy integrations, and sandboxed Python execution.
• Implement error-handling and self-correction mechanisms.
• Build evaluation frameworks to measure trajectory efficiency and safety.
• Set up monitoring to visualize the agent’s decision process and detect breakdowns.

Skills

Production ML

Agentic architectures

LLM prompts

Cloud deployment

API design

LangChain/OpenAI

LangGraph

Vector DBs

Education

Bachelor’s or Master’s in CS/Stats/Math

Tools

LangChain

LangGraph

OpenAI

Claude

Pinecone

Milvus

Qdrant

AWS

GCP

Azure

Job description

Factored is seeking a Senior Machine Learning Engineer focused on agentic workflows. You will design and deploy systems that convert LLMs into autonomous agents capable of planning, tool-use, and self-correction.

You will architect task decomposition, memory management, and robust interfaces between LLMs and APIs, with guardrails and HITL checks for high-stakes decisions, in a fully remote environment. You will collaborate with data scientists and software engineers across time zones, and