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Salary: USD 300,000 - 400,000 / annual
Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. The company's first goal is to democratize frontier AI R&D across scientific disciplines by building a frontier AI research company and training models end-to-end.
As a Member of Technical Staff, Distributed Systems Engineer, you will work on systems that determine how much goodput the company extracts from its compute infrastructure. You will identify and resolve bottlenecks across the technology stack, working at the intersection of AI workloads and system design.
Key areas of focus include:
- AI workload and system co-design: rethink the AI application, execution, and hardware choices together to improve performance across the stack
- Performance engineering: profile across the stack, find where time and resources go, and fix what matters
- Reliable distributed systems: design how work and data move across machines, balancing parallelism, communication, and correctness under load and failure
- Heterogeneous compute and workloads: make large-scale mixes of sandboxes, agent harnesses, training, and inference run efficiently together across CPUs and accelerators
This is a high-impact role where your work directly influences the efficiency and capability of the company's AI research infrastructure.
Requirements: Not explicitly stated in the posting. Candidates should have strong expertise in distributed systems, performance engineering, and systems-level optimization, with demonstrated experience in large-scale compute environments.
