Member of Technical Staff - Distributed Training Engineer

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

We need someone who:

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

What We Offer

Greenfield challenges: Build systems from scratch for novel architectures. High ownership from day one.

Compensation: Competitive base salary with equity in a unicorn-stage company

Health: We pay 100% of medical, dental, and vision premiums for employees and dependents

Financial: 401(k) matching up to 4% of base pay

Time Off: Unlimited PTO plus company-wide Refill Days throughout the year