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
At Liquid AI, we’re building novel model architectures that require GPU performance engineering beyond what standard frameworks can deliver.
You’ll design, implement, and optimize custom GPU kernels that make our models faster and more efficient across training, post-training, and inference. Working closely with researchers and engineers, you’ll identify performance bottlenecks, translate architectural innovations into efficient implementations, and deliver measurable improvements in production workloads.
This is a hands-on role for someone who enjoys understanding how GPUs actually execute code, from memory access patterns and warp scheduling to tensor core utilization. You’ll have the ownership to investigate complex performance problems, make informed engineering tradeoffs, and ship optimizations that directly impact how our models perform.
What We’re Looking For
We need someone who:
Reasons from first principles: You understand GPU architecture and can explain why a workload is compute-bound or memory-bound, how latency and throughput interact, and which optimizations are likely to matter.
Builds at the kernel level: You’re comfortable writing, debugging, and optimizing GPU kernels using CUDA or Triton. You understand the implementation details, not just the abstractions.
Optimizes systematically: You form hypotheses, use profiling and benchmarks to identify bottlenecks, evaluate tradeoffs, and validate improvements against real workloads.
Turns research into performance: You can translate novel model architectures and research ideas into efficient, correct, and maintainable implementations.
Owns problems end-to-end: You can take an ambiguous performance challenge from investigation through implementation, integration, benchmarking, and production deployment.
Communicates technical decisions clearly: You explain your reasoning, challenge assumptions constructively, and collaborate effectively with researchers and engineers.
The Work
Design, implement, and optimize high-performance GPU kernels for Liquid AI’s novel model architectures.
Profile training, post-training, and inference workloads to identify bottlenecks and improve latency, throughput, and hardware utilization.
Optimize memory access patterns, thread and block configurations, register usage, shared memory, and tensor core utilization.
Integrate custom kernels into PyTorch pipelines through custom operators, extensions, and appropriate dispatch mechanisms.
Evaluate numerical precision, correctness, and performance tradeoffs across different workloads and hardware configurations.
Build and maintain benchmarking infrastructure, correctness tests, and performance regression checks.
Collaborate with researchers to translate architectural innovations into production-ready GPU implementations.
Desired Experience
Must-have:
Hands-on experience developing and optimizing custom GPU kernels using CUDA or Triton, beyond relying exclusively on high-level libraries.
Strong understanding of GPU architecture, including memory hierarchy, thread execution, warps, shared memory, register pressure, occupancy, and compute versus memory bandwidth limitations.
Demonstrated ability to reason about GPU performance, identify bottlenecks, and make informed optimization decisions.
Experience profiling, benchmarking, and debugging GPU workloads using tools such as Nsight Systems, Nsight Compute, or equivalent tooling.
Strong C++ programming skills and experience working with performance-sensitive code.
Nice-to-have:
Experience with CUTLASS, tensor cores, or advanced matrix multiplication optimizations.
Experience integrating custom GPU kernels into PyTorch or other ML frameworks.
Experience optimizing deep learning workloads across training, post-training, or inference.
Familiarity with numerical precision tradeoffs, mixed-precision computation, and accumulation strategies.
Experience developing benchmark harnesses, automated performance tests, or regression detection infrastructure.
Experience optimizing nonstandard model architectures or translating research implementations into production systems.
What Success Looks Like (Year One)
Deliver measurable improvements in latency, throughput, or hardware efficiency for critical model workloads, validated through repeatable end-to-end benchmarks.
Independently identify and resolve significant GPU performance bottlenecks, from initial profiling through production deployment.
Ship and maintain custom kernels that improve the performance of Liquid AI’s model architectures.
Establish reliable performance benchmarks and regression checks that help the team maintain improvements as models and workloads evolve.
Become a trusted technical partner to researchers and engineers on GPU performance decisions.
What We Offer
Unique challenges: Our architectural innovations and efficiency requirements offer unique optimization challenges. 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.