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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.
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Worksite address
san francisco, CA, 94199, US
Who can apply
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