# Member of Technical Staff, Research Engineer (GPU Performance)

**Company:** [Runway](https://hotfix.jobs/companies/runway)
**Location:** Remote
**Role:** AI Research
**Salary:** $270k – $370k/yr
**Experience:** 4+ years
**Skills:** CUDA, Triton, Cutlass, Nccl, PyTorch, JAX, Fsdp, Fp8, Bf16, Int8
**Posted:** 2022-04-29

> Research Engineer optimizes GPU performance for large-scale AI world model training and real-time inference, focusing on custom kernels, distributed systems, and mixed-precision techniques. Requires 4+ years in ML infrastructure and performance optimization.

## Job Description

## Responsibilities
- Optimize training throughput across large GPU clusters — improving MFU through custom kernels, mixed-precision strategies (FP8, BF16), memory-efficient attention, and activation checkpointing
- Design and maintain distributed training infrastructure: tensor parallelism, context parallelism, FSDP, and fault-tolerant multi-node setups
- Profile and accelerate inference pipelines for real-time multimodal generation — CUDA graph compilation, KV cache optimization, operator fusion, and latency reduction
- Optimize and scale training infrastructure to improve efficiency and reliability
- Contribute to the entire stack, from low-level kernel optimizations to high-level model design

## Requirements
- 4+ years of experience in systems engineering, ML infrastructure, or performance optimization for deep learning
- Familiarity with GPU kernel development (**CUDA**, **Triton**, **CUTLASS**) and distributed systems (**NCCL**, collective communication, model parallelism)
- Experience with ML framework internals (**PyTorch**, **JAX**) and mixed-precision / low-precision techniques (**FP8**, **INT8**)
- Experience building and operating large-scale training infrastructure, including fault tolerance and cluster orchestration
- Excitement about building AI that simulates the world — and making it performant enough to run in real time

## Nice-to-haves
- Experience with torch’s compilation feature

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