Member of Technical Staff - Distributed Training Engineer

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

Our Training Infrastructure team is building the distributed systems that power our next-generation Liquid Foundation Models. As we scale, we need to design, implement, and optimize the infrastructure that enables large-scale training.

This is a high-ownership training systems role focused on runtime/performance/reliability (not a general platform/SRE role). You’ll work on a small team with fast feedback loops, building critical systems from the ground up rather than inheriting mature infrastructure.

We need someone who:

• Hands-on experience building distributed training infrastructure (PyTorch Distributed DDP/FSDP, DeepSpeed ZeRO, Megatron-LM TP/PP)

• Experience diagnosing performance bottlenecks and failure modes (profiling, NCCL/collectives issues, hangs, OOMs, stragglers)

• Understanding of hardware accelerators and networking topologies

• Experience optimizing data pipelines for ML workloads

• MoE (Mixture of Experts) training experience

• Large-scale distributed training (100+ GPUs)

• Open-source contributions to training infrastructure projects

• Training throughput has increased

• Overall training efficiency/cost has improved

• Training stability has improved (fewer failures, faster recovery)

• Data loading bottlenecks are eliminated for multimodal workloads