Cade Daniel
Founding Member Of Technical Staff at Stealth
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolCade Daniel is a systems-focused software engineer with 10 years of experience building high-performance distributed systems and ML infrastructure, currently a founding member of technical staff in San Francisco. He has driven model-parallel and inference optimizations at AWS, Anyscale, and Databricks—delivering measurable wins like a 10% reduction in GPT-3 training time on 1024 A100s and numerous low-latency, memory-efficient LLM inference innovations. An active open-source contributor, Cade worked on vLLM’s speculative decoding and integration with LLMEngine as well as Ray documentation and tooling, helping scale and harden production ML workflows. He combines deep systems-level know-how (RDMA, activation offloading, allreduce) with pragmatic shipping experience across startups and cloud platforms, and often focuses on reducing end-to-end training and serving latency in non-obvious ways such as offloading reductions to CPU nodes.
10 years of coding experience
9 years of employment as a software developer
Bachelor’s Degree, Computer Science / Math Minor, Bachelor’s Degree, Computer Science / Math Minor at Brigham Young University
English, Spanish
Stackoverflow
561reputation
129kreached
19answers
0questions
Github Skills (28)
pytorch10
python10
testing10
inference10
llm10
ray10
documentation10
machine-learning9
cluster-manager9
clustering9
cluster-api9
transformer8
cuda8
cli8
api-documentation8
Programming languages (6)
Github contributions (5)
vllm-project/vllm
Jun 2023 - Feb 2025
A high-throughput and memory-efficient inference and serving engine for LLMs
Role in this project:
ML Engineer
ML EngineerContributions:297 reviews, 69 PRs, 36 pushes in 1 year 8 months
Contributions summary:Cade contributed to the vLLM project, focused on optimizing and testing machine learning inference for large language models. Their work involved implementing and refining core components like rejection samplers and multi-step workers for speculative decoding, a technique to accelerate inference. They wrote comprehensive tests to ensure the correctness and performance of the speculative decoding and overall system. Their changes also included integration with the LLMEngine, adding features like target-model logprobs.
amdcudadeepseekgpthpu
ray-project/ray
Jul 2022 - Jan 2023
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:Back-end Developer & Technical Writer
Back-end Developer & Technical WriterContributions:1 release, 352 reviews, 59 commits in 6 months
Contributions summary:Cade's contributions primarily involve enhancing the documentation for Ray clusters. The commits introduce a new "Ray Clusters (Under Construction)" section, restructuring existing documentation to align with a new format. This includes porting existing content, creating new pages, and updating references. The user also added code examples and guides for setting up and running Ray clusters on VMs, including CLI and SDK instructions for job submission.
pythonconsistsruntimetensorflowserving
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