Senior ML Engineer — Frontier LLM Systems & Ownership

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

Benefits offered by this job

Equity

Ownership over systems

Compute resources

Job summary

Harrison Clarke in San Francisco is seeking a Member of Technical Staff - Machine Learning Engineer to push the frontier of agentic AI systems. You will design and deploy end-to-end agent architectures, balancing deep ML insights with robust systems engineering.

You will own data/knowledge layers, improve reliability, and collaborate across research, product, and infra teams to align model behavior with real-world environments.

Qualifications

• Strong intuition for machine learning and LLM systems.
• Experience building and deploying LLM-powered applications or agents under real-world reliability and performance constraints.
• Ability to reason from first principles and debug complex, stochastic, and distributed system failures.
• Comfort operating across research, product, and infrastructure boundaries in fast-moving environments.
• Excellent software engineering fundamentals, including ownership of production-quality codebases and collaborative development practices.

Responsibilities

• Design, build and iterate on agent architectures: planning, tool use, routing, memory, verification, and long-horizon execution.
• Own the data/knowledge layer that agents depend on.
• Improve performance and reliability.
• Collaborate with post-training and evaluation to align model behavior with the environments the agents run in.

Skills

LLM systems

ML intuition

Production software engineering

Distributed systems debugging

Cross-functional collaboration

Job description

Harrison Clarke in San Francisco is seeking a Member of Technical Staff - Machine Learning Engineer to push the frontier of agentic AI systems. You will design and deploy end-to-end agent architectures, balancing deep ML insights with robust systems engineering.

You will own data/knowledge layers, improve reliability, and collaborate across research, product, and infra teams to align model behavior with real-world environments.