Member of Technical Staff - Distributed Training Engineer
Liquid AI · San Francisco
CHECKED 23H AGO
Frontier labremote
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