# Member of Technical Staff, Systems Infrastructure

**Company:** [Fireworks AI](https://hotfix.jobs/companies/fireworks-ai)
**Location:** San Mateo, CA, New York, NY
**Role:** DevOps / SRE
**Salary:** $200k – $230k/yr
**Experience:** 0+ years
**Skills:** C++, Rust, Go, Python, Kubernetes, CUDA, Rocm, Triton, Rdma, InfiniBand, Nccl, Rccl, Distributed Systems, Gpu Scheduling, Nvme
**Posted:** 2026-09-08

> Build and operate large-scale scheduling, storage, caching, and networking infrastructure for AI training and inference. The role targets PhD researchers graduating by December 2026 with systems research depth and strong programming and performance-measurement skills.

## Job Description

## Responsibilities
- Design, build, and operate core infrastructure systems for large-scale training and inference.
- Build benchmarks, traces, and simulators to evaluate scheduling, caching, and network performance.
- Identify and eliminate bottlenecks across the stack, from kernels and drivers to scheduler policies.
- Turn systems research ideas into production systems that withstand real workloads, failures, and customers.
- Collaborate with research and inference teams to align infrastructure and model design.
- Track emerging hardware, interconnects, and systems research to inform technical direction.

## Areas of Focus
- **Scheduling and resource management:** GPU job scheduling across heterogeneous hardware, multi-tenant isolation, fair sharing, preemption, topology- and locality-aware placement, autoscaling, fleet utilization, and capacity planning.
- **Distributed storage and caching:** Storage and caching for model weights, checkpoints, datasets, and KV cache; tiering across memory, local NVMe, and object storage; cache admission and eviction, consistency, cold-start, and weight-loading performance.
- **Datacenter networking:** RDMA/RoCE and InfiniBand, collective communication performance, congestion control, topology design, load balancing, tail latency, and reliability at fleet scale.

## Requirements
- PhD completed within the last six months or expected by December 2026 in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
- Research background in distributed systems, operating systems, scheduling and resource management, storage systems, computer networks, computer architecture, or high-performance computing.
- Depth in at least one focus area, demonstrated through a dissertation, publications, or systems built.
- Strong systems programming skills in C/C++, Rust, Go, or a similar language, plus working proficiency in Python.
- Experience building and evaluating real systems, not only simulations, and rigorously measuring performance.
- Ability to communicate systems designs and results clearly to varied audiences.

## Preferred Qualifications
- First-authored publications at top-tier systems or networking venues.
- Infrastructure, cloud, or HPC internships, or meaningful open-source systems contributions.
- Experience with GPU clusters, CUDA, ROCm, Triton, or heterogeneous compute environments.
- Kubernetes and cloud infrastructure experience, or experience operating multi-tenant production clusters.
- Performance profiling and debugging experience in distributed environments.
- Research or engineering achievements demonstrated through grants, fellowships, patents, or systems competitions.

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