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Member of Technical Staff - Distributed Training Engineer

Liquid AI · San Francisco

CHECKED 23H AGO

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At a glance

Highlights

  • Build distributed systems powering next-generation Liquid Foundation Models.
  • Design core systems that keep large training runs fast, stable and reliable.
  • Build greenfield training infrastructure from scratch with high ownership.

What they offer

  • Competitive base salary with equity in a unicorn-stage company
  • 100% of medical, dental, and vision premiums for employees and dependents
  • 401(k) matching up to 4% of base pay
  • Unlimited PTO plus company-wide Refill Days throughout the year

Ideal candidate

Builder who loves distributed systems complexity and thrives in ambiguity. Aligns with team priorities, delivers reliably, and takes pride in fast, robust infrastructure for evolving architectures.

Requirements

  • 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

Lab profile

About Liquid AI

MIT CSAIL spinout building efficient general-purpose AI for cloud to edge.

What they build

  • Liquid Foundation Models
  • LFM2.5-Audio

Known for

  • Efficiency-first foundation models
  • Data center to on-device deployment
  • Low latency with minimal memory usage
  • Liquid Neural Networks architecture

Key facts

  • Spun out of MIT CSAIL
More about Liquid AI →
Apply on the lab’s site ↗