Machine Learning Systems Engineer at Nuance Labs

Department: Engineering

Compensation

$300,000 – $400,000/yr

Location: Seattle, Washington
Type: Full-time
Level: senior
Posted: Jun 5, 2026

Description

Nuance Labs is hiring a Machine Learning Systems Engineer to own distributed training infrastructure for large-scale omni model pretraining. The role builds and operates the training runtime, including job orchestration, distributed execution, checkpointing, recovery, monitoring, debugging, GPU communication, and performance optimization across large GPU clusters. It also develops infrastructure for multimodal data loading, temporal alignment, variable sequence handling, and memory-efficient training. The position requires hands-on experience with large-scale distributed training, major training stacks, and omni or multimodal systems, and offers a Seattle-based in-person role with visa sponsorship.

Required Skills

Distributed training
GPU communication
Checkpointing
NCCL
PyTorch FSDP
DeepSpeed
Multimodal synchronization
Performance debugging

Additional Information

Required Previous Experiences: Hands-on experience running large-scale distributed training jobs across large GPU clusters, including hundreds of GPUs.
Deep understanding of distributed training mechanics, including parallelism, gradient communication, collectives, mixed precision, checkpointing, optimizer state, and memory pressure.
Practical experience with at least one major large-scale training stack such as Megatron, PyTorch FSDP, or DeepSpeed.
Understanding of omni or multimodal training challenges involving audio, video, language, long temporal context, variable sequence lengths, and high-throughput data loading.
Preferred Qualifications: Prior 0→1 experience building large-scale training infrastructure or modifying core training frameworks, runtimes, checkpointing, or debugging systems.
Experience training large omni or multimodal models involving audio, video, text, or long-context temporal data.
Experience with adjacent infrastructure areas such as reinforcement learning, post-training, data infrastructure, synthetic data generation, evaluation, or serving.
Publications or substantial open-source contributions in machine learning systems, distributed systems, high-performance computing, GPU performance, or training infrastructure.