Member of Technical Staff, Systems Infrastructure (2026 PhD New Grad)

THE ROLE:

This role is designed for systems researchers finishing their PhD who want to see their ideas run on real fleets at real scale. As a Member of Technical Staff on the Systems Infrastructure team, you'll design and build the substrate underneath Fireworks — the schedulers, storage systems, and networks that keep tens of thousands of accelerators busy and inference latency low.

The problems here are the ones your dissertation probably touched: how to place compute-intensive jobs across heterogeneous hardware without stranding capacity, how to move model weights and KV cache fast enough that they never become the bottleneck, and how to keep a datacenter network saturated with collective traffic without collapsing tail latency. The difference is that here you get a production fleet as your testbed and your work ships.

You'll be paired with a senior engineer as a mentor and given a real problem from day one. Start dates are flexible around thesis defense timelines.

AREAS OF FOCUS

We're hiring across three areas — depth in any one of them is what we're looking for, not all three:

• Scheduling & resource management: GPU job scheduling and scheduling of compute-intensive workloads across heterogeneous hardware; multi-tenant isolation, fair sharing and preemption, topology- and locality-aware placement, autoscaling, fleet utilization, and capacity planning across accelerator generations and vendors

• Distributed storage & caching: High-performance distributed storage and caching for model weights, checkpoints, datasets, and KV cache; tiering across memory, local NVMe and object storage, cache admission and eviction policy, consistency, and fast cold-start and weight-loading paths

• Datacenter networking: High-performance DC networks for AI workloads; RDMA/RoCE and InfiniBand, collective communication (NCCL/RCCL) performance, congestion control, topology design, load balancing, and tail-latency and reliability engineering at fleet scale

KEY RESPONSIBILITIES

• Design, build, and operate core infrastructure systems for large-scale training and inference

• Model and measure system behavior — build the benchmarks, traces, and simulators needed to reason about scheduling, caching, and network performance before committing to a design

• Identify bottlenecks across the stack, from kernel and driver to scheduler policy, and drive them out with data

• Turn research ideas into production systems that hold up under real workloads, real failures, and real customers

• Work closely with the research and inference teams so that infrastructure design and model design inform each other

• Contribute to the team's technical direction by tracking emerging hardware, interconnects, and systems research

MINIMUM QUALIFICATIONS:

• PhD completed within the last 6 months, or expected completion by December 2026, in Computer Science, Computer Engineering, Electrical Engineering, or a similar field

• Research background in one or more of: distributed systems, operating systems, scheduling and resource management, storage systems, computer networks, computer architecture, or high-performance computing

• Depth in at least one of the three focus areas above, demonstrated through your dissertation, publications, or systems you've 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 simulation — and reasoning rigorously about performance with measurements

• Ability to communicate systems design and results clearly to audiences with different backgrounds

PREFERRED QUALIFICATIONS:

• First-authored publications at top-tier systems or networking venues (OSDI, SOSP, NSDI, EuroSys, SIGCOMM, ATC, FAST, ASPLOS, MLSys, SC, or similar)

• Industry internships in infrastructure, cloud, or HPC, or meaningful open-source contributions to systems projects (Kubernetes, Ray, Slurm, vLLM, NCCL, DPDK/SPDK, Ceph, or similar)

• Hands-on experience with GPU clusters, accelerator programming (CUDA, ROCm, Triton), or heterogeneous compute environments

• Experience with Kubernetes and cloud infrastructure, or with operating multi-tenant clusters in production

• Experience with performance profiling and debugging in distributed environments

• Research or engineering experience demonstrated via grants, fellowships, patents, or systems competitions

Why Fireworks?

• Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.