Machine Learning Engineer

Harrison Clarke; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Harrison Clarke is partnering with an early‑stage AI infrastructure company to hire a highly technical Machine Learning Engineer. This hands‑on role sits at the intersection of LLM systems, AI agents, inference and performance engineering, focusing on production workloads and system optimization.

You’ll contribute across AI infrastructure, agent systems, inference optimization, distributed execution and runtime performance, tackling bottlenecks and shaping the architecture as one of the first

Qualifications

• Strong software engineering and computer science fundamentals.
• Experience building complex production systems.
• Depth in distributed systems, performance engineering, runtimes, databases, compilers, or AI infrastructure.
• Meaningful experience with modern LLM or agent systems.
• Ability to operate across multiple layers of the stack.
• Comfortable in a highly ambiguous, early‑stage environment.

Responsibilities

• Design and build infrastructure for production AI and agent workloads.
• Improve the performance, efficiency and reliability of AI systems.
• Work across inference, execution, runtime and distributed systems challenges.
• Identify bottlenecks across complex production workloads and develop systems‑level solutions.
• Build new infrastructure from first principles rather than simply integrating existing tools.
• Help shape core technical architecture as one of the earliest engineers.

Skills

Strong software engineering

Production systems

Distributed systems

Performance engineering

AI infrastructure

Ownership mindset

Job description

We’re partnering with an early-stage AI infrastructure company building foundational technology for the next generation of AI and agent-based systems.

The team is looking for a highly technical Machine Learning Engineer to work at the intersection of LLM systems, AI agents, inference and performance engineering. This is a hands-on engineering role focused on understanding and improving how production AI workloads execute, scale and perform.

You’ll work across AI infrastructure, agent systems, inference optimization, distributed execution and runtime performance. This is particularly well suited to engineers who enjoy going deeper than simply consuming models or agent frameworks and want to work on the underlying systems that determine how AI workloads behave and perform.

Responsibilities:

• Design and build infrastructure for production AI and agent workloads
• Improve the performance, efficiency and reliability of AI systems
• Work across inference, execution, runtime and distributed systems challenges
• Identify bottlenecks across complex production workloads and develop systems‑level solutions
• Build new infrastructure from first principles rather than simply integrating existing tools
• Help shape core technical architecture as one of the earliest engineers

What we're looking for:

• Strong software engineering and computer science fundamentals
• Experience building complex production systems
• Depth in areas such as distributed systems, performance engineering, runtimes, databases, compilers, inference or AI infrastructure
• Meaningful experience working with modern LLM or agent systems
• Ability to operate across multiple layers of the stack
• Comfortable working in a highly ambiguous, early‑stage environment
• Strong ownership mentality and interest in building from zero to one

You do not need to have trained foundation models or come from a traditional ML research background. We're particularly interested in strong systems engineers who have moved deeper into AI infrastructure and agent systems.

Previous startup experience is helpful but not required. Technical depth, curiosity and the ability to build are more important than title or years of experience.