About the RoleWe're looking for a deeply technical MTS to own distributed training infrastructure for large-scale omni model pretraining.
This role sits at the intersection of research, systems, and GPU-scale execution - building the training stack from 01 and scaling it: distributed execution, parallelism, GPU communication, data loading, checkpointing, observability, and debugging.
Our models are omni from the ground up (audio, video, language, real-time full-duplex), which introduces systems challenges beyond standard LLM training: multimodal synchronization, long temporal context, variable sequence lengths, and tight memory/throughput constraints.
High ownership. Direct impact on what models we can train, how fast research can iterate, and how reliably we scale.
What You'll Own- Own the distributed training stack for omni model pretraining, from 01 system design to 110 scaling across large GPU clusters.
- Build and operate the core training runtime: job orchestration, distributed execution, checkpointing, recovery, monitoring, and debugging for long-running training jobs.
- Optimize large-scale training performance across parallelism strategy, GPU communication, memory usage, data throughput, MFU, step time, and end-to-end training efficiency.
- Build infrastructure for omni training workloads: high-throughput audio/video/text data loading, temporal alignment, variable sequence handling, multimodal synchronization, and memory-efficient training.
- Evolve the platform as model architectures, training recipes, data mixtures, sequence lengths, hardware constraints, and research directions change.
What We're Looking For- Hands-on experience running large-scale distributed training jobs across large GPU clusters; experience at hundreds of GPUs minimum, 1,000+ GPUs a strong plus.
- Deep understanding of distributed training mechanics: data/tensor/pipeline/sequence parallelism, gradient communication, collectives, mixed precision, activation checkpointing, optimizer state, memory pressure, and framework-level tradeoffs.
- Strong understanding of GPU communication and performance debugging: NCCL, all-reduce/all-gather/reduce-scatter, communication-computation overlap, topology, synchronization, stragglers, low MFU, OOMs, checkpoint bottlenecks, and data starvation.
- Practical experience with at least one major large-scale training stack such as Megatron, PyTorch FSDP, DeepSpeed, or equivalent internal infrastructure.
- Understanding of omni or multimodal training challenges, especially audio/video/language data, long temporal context, variable sequence lengths, modality-specific bottlenecks, and high-throughput dataloading.
- Strong software engineering fundamentals, curiosity, and adaptability to new model architectures, training frameworks, hardware constraints, and research ideas.
Bonus Points- Prior 01 experience building large-scale training infrastructure or deeply 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 RL/post-training, data infrastructure, synthetic data generation, evaluation, or serving.
- Publications or substantial open-source contributions in ML systems, distributed systems, HPC, GPU performance, or training infrastructure.
Compensation$300,000 - $400,000 base salary, plus meaningful equity. We think long-term ownership matters and structure equity accordingly.
Logistics- Location: In-person in Seattle, 5 days a week - we believe in the compounding value of working shoulder-to-shoulder
- Health: HSA plan with ~$2,000 in company contributions - about 2x what most big tech companies offer
- PTO: 15 days + public holidays, and we close for a full week over the holidays
- Lunch, beverages, and snacks: On us, every workday - the kind of thing that makes you actually look forward to the workday
- Commuter benefits
- 401K: In the works
