Member of Technical Staff, Distributed Systems Engineer

Mirendil Inc.; San Francisco (CA); On-site; USD 300,000 - 400,000; Full time

Benefits offered by this job

Equity grant

Job summary

Mirendil Inc. is seeking a Systems Engineer in San Francisco to locate bottlenecks and optimize how compute is utilized. You will influence AI workload design, system co-design, and data movement across machines to maximize goodput while ensuring correctness under load and failure.

The role emphasizes performance engineering for heterogeneous CPU and accelerator workloads. The company offers a base salary of $300,000–$400,000 with a meaningful equity grant and competitive benefits, reflecting

Responsibilities

• 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

Job description

Mirendil

Mirendil is a tech‑first company focused on solving core bottlenecks that unlock step‑change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We are building a frontier AI research company and training our own models end‑to‑end.

The Role

We have a lot of compute, and a lot more work for it. We’re looking for a Systems Engineer who knows how to find bottlenecks and move them somewhere we can live with. Your work determines how much goodput we get out of our compute. Some example areas you might work on:

• 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

We offer a base salary of $300,000–$400,000 and a meaningful equity grant, depending on experience and background, along with competitive benefits.