About Liquid AI
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.
The Opportunity
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.
What We're Looking For
- Loves distributed systems complexity: Our team builds systems that keeps long training runs stable, debugs training failures across GPU clusters, and improves performance.
- Wants to build: We need builders who find satisfaction in robust, fast, reliable infrastructure.
- Thrives in ambiguity: Our systems support model architectures that are still evolving. We make decisions with incomplete information and iterate quickly.
- Aligns with team priorities and delivers: Our best engineers align with team priorities while pushing back with data when they see problems.
The Work
- Design and build core systems that make large training runs fast and reliable
- Build scalable distributed training infrastructure for GPU clusters
- Implement and tune parallelism/sharding strategies for evolving architectures
- Optimize distributed efficiency (topology-aware collectives, comm/compute overlap, straggler mitigation)
- Build data loading systems that eliminate I/O bottlenecks for multimodal datasets
- Develop checkpointing mechanisms balancing memory constraints with recovery needs
- Create monitoring, profiling, and debugging tools for training stability and performance
Desired Experience
Must-have:
- 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
Nice-to-have:
- MoE (Mixture of Experts) training experience
- Large-scale distributed training (100+ GPUs)
- Open-source contributions to training infrastructure projects
What Success Looks Like (Year One)
- 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