Machine learning Empleos en Berkeley ca

Overview

We are building a distributed LLM inference network that combines idle GPU capacity from around the world into a single cohesive plane of compute for running large-language models like DeepSeek and Llama 4. At any given moment, we have over 5,000 GPUs and hundreds of terabytes of VRAM connected to the network. We are a small, well-funded team working on difficult, high-impact problems at the intersection of AI and distributed systems. We primarily work in-person from our office in downtown San Francisco.

Responsibilities

Design and implement optimization techniques to increase model throughput and reduce latency across our suite of models

Deploy and maintain large language models at scale in production environments

Deploy new models as they are released by frontier labs

Implement techniques like quantization, speculative decoding, and KV cache reuse

Contribute regularly to open source projects such as SGLang and vLLM

Deep dive into underlying codebases of TensorRT, PyTorch, TensorRT-LLM, vLLM, SGLang, CUDA, and other libraries to debug ML performance issues

Collaborate with the engineering team to bring new features and capabilities to our inference platform

Develop robust and scalable infrastructure for AI model serving

Create and maintain technical documentation for inference systems

Requirements

3+ years of experience writing high-performance, production-quality code

Strong proficiency with Python and deep learning frameworks, particularly PyTorch

Demonstrated experience with LLM inference optimization techniques

Hands-on experience with SGLang and vLLM, with contributions to these projects strongly preferred

Familiarity with Docker and Kubernetes for containerized deployments

Experience with CUDA programming and GPU optimization

Strong understanding of distributed systems and scalability challenges

Proven track record of optimizing AI models for production environments

Nice to Have

Familiarity with TensorRT and TensorRT-LLM

Knowledge of vision models and multimodal AI systems

Experience implementing techniques like quantization and speculative decoding

Contributions to open source machine learning projects

Experience with large-scale distributed computing

Compensation

We offer competitive compensation, equity in a high-growth startup, and comprehensive benefits. The base salary range for this role is $180,000 - $250,000, plus competitive equity and benefits including :

Full healthcare coverage

Quarterly offsites

Flexible PTO

Skills : pytorch, gpu optimization, deep learning frameworks, sglang, vllm, cuda programming, machine learning, python, llm

#J-18808-Ljbffr