Member of Technical Staff, Distributed Systems Engineer
Distributed systems engineer at Mirendil, a frontier AI research company training its own models end-to-end. The role maximizes useful output from a large compute fleet through AI workload/system co-design, full-stack performance profiling, reliable distributed systems design, and running heterogeneous training/inference workloads across CPUs and accelerators. On-site in San Francisco; $300k-$400k
- Compensation
- $300K–$400K / yr
- Location
- San Francisco
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Systems engineering experience finding and resolving performance bottlenecks across the stack
Nice to have
- Experience with distributed systems design balancing parallelism, communication, and correctness under load and failure
- Experience running large-scale mixed workloads (sandboxes, agent harnesses, training, inference) across CPUs and accelerators
Full 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.