Member of Technical Staff, Distributed Systems Engineer
AI in this role
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.
How we rate this
Member of Technical Staff, Distributed Systems Engineer at Mirendil rates 89 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a research question you investigated. What did you find?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
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- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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