Desired Experience

• PyTorch
• Python
• Model-serving frameworks (e.g. TensorRT, vLLM, SGLang)

You're A Great Fit If

• You have experience building large-scale production stacks for model serving.
• You have a solid understanding of ragged batching, dynamic load balancing, KV-cache management, and other multi-tenant serving techniques.
• You have experience with applying quantization strategies (e.g., FP8, INT4) while safeguarding model accuracy.
• You have deployed models in both single-GPU and multi-GPU environments and can diagnose performance issues across the stack.

What You'll Actually Do

• Optimize and productionize the end-to-end pipeline for GPU model inference around Liquid Foundation Models (LFMs).
• Facilitate the development of next-generation Liquid Foundation Models from the lens of GPU inference.
• Profile and robustify the stack for different batching and serving requirements.
• Build and scale pipelines for test-time compute.

What You'll Gain

• Hands-on experience with state-of-the-art technology at a leading AI company.
• Deeper expertise in machine learning systems and efficient large model inference.
• Opportunity to scale pipelines that directly influence user latency and experience with Liquid's models.
• A collaborative, fast-paced environment where your work directly shapes our products and the next generation of LFMs.