Senior Machine Learning Engineer

Harrison Clarke; San Francisco (CA); Presencial; USD 180 000 - 280 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity

Ownership over systems

Compute resources

Resumo da oferta

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.

Qualificações

• 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.

Responsabilidades

• 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.

Conhecimentos

LLM systems

ML intuition

Production software engineering

Distributed systems debugging

Cross-functional collaboration

Descrição da oferta de emprego

Role: Member of Technical Staff - Machine Learning Engineer

We are working with a well funded frontier AI lab, currently operating in stealth, who are building agentic systems around frontier LLMs. This role blends deep ML intuition with strong systems engineering. You will join a small but elite and fast growing technical team, which offers real ownership of end-to-end outcomes and the chance to join a lab which is building the next generation of AI systems.

The role

• 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 enviroments the agents run in

What they're looking for

• 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.

What is on offer:

• Top-of-market compensation + meaningful equity
• Ownership over high-leverage systems at the frontier
• Strong compute/tooling support