> Markdown version of [/jobs/ext/4549635-machine-learning-researcher](https://www.wearedevelopers.com/jobs/ext/4549635-machine-learning-researcher). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Researcher

- **Company:** Openreq
- **Location:** Cupertino, CA, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Nvidia CUDA, Graph Database, High-Level Architecture, Python (Programming Language), Search Algorithms, Machine Learning, Recommender Systems, Graphics Processing Unit (GPU), Pytorch, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Llamaindex
- **Published:** November 3, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-researcher-cupertino-ca--2f03e24e-7170-4c31-b6bc-8d90772c1580

## About the Role

Machine Learning, Research Skills, GPU (Graphics Processing Unit), Tree Search, Search Algorithms, Market Research, Search Agent, Quality Assurance, Benchmarking, Quality Management, Strong candidates may also have experience with: Knowledge graphs Name entity recognition Tool calling and coder LLMs SWE bench & SWE agent
We encourage you to apply even if you do not believe you meet every single qualification.

## Description

With Etched ASICs, we have fundamentally different constraints than existing AI chips. We don'thave the same batch-size-latency tradeoff as GPUs. We can parallelize workloads and digestlarge contexts much more efficiently than GPUs.
Sohu enables entirely new research directions and products. When our chips come out, theseuse cases need to already be mature and visceral. Whether real-time video, agents, speculativedecoding, or new tree search algorithms, we must create the market for our hardware.In this role, you will lead efforts on research directions that trade compute for improvedunderstanding and speed, particularly with agents and new search techniques.
Representative projects Combine multi-agent with RAG to improve the quality of QA Evaluate new system on standard benchmark Design new and verifiable benchmark for agent reasoning Design LLM content understanding based recommendation systems
You may be a good fit if you have: Past projects or publications with substantial impact in ML and/or CV (quality > quantity) Strong hands-on engineering skills, particularly with python, pytorch, CUDA, DDP/FSDP Deep understanding of open and closed source model architectures and open sourcelibraries for transformer training and inference Familiarity with LlamaIndex, LangChain, MetaGPT, CoT, ToT, and beam search Ability to think outside the box and make tradeoffs considering feasibility, quality, andtime-to-ship of a project