Architect
Member of Technical Staff - Research Intern
Palo Alto · Staff+ · Internship
Sponsorship not specifiedDetected 161 days ago
PythonAlgorithmsMachine LearningDeep LearningPyTorchNLPLLMsVerilogHardware DesignResearchMentoring
About the role
- Born out of Stanford, our team blends researchers and engineers from Anthropic, DeepMind, Meta, Apple, Intel, and other frontier labs.
- You should be able to write clean, efficient research code. - Research Mindset: A fast learner who is comfortable navigating ambiguity.
Responsibilities
- As a Research Intern at Architect, you will spend 3 months working alongside the founding team to push the boundaries of how AI models explore and optimize hardware designs.
- Implement and test new algorithms for model fine-tuning and evaluation, helping to translate research papers into working prototypes.
- Architect is an AI research and product lab for chip design.
- We build AI models and systems that can explore, design, optimize, and verify new hardware.
- Our goal is to reimagine chip design using AI, cut down ASIC design time and cost, and enable a new era of ultra-efficient, domain-specific chips powering the future of computation.
- Backed by leading VCs and angels, including the Chief Scientist at Google, Stanford professors, and founders of chip companies, Architect operates in stealth, pushing the limits of AI4EDA and building the intelligence layer for the hardware revolution.
Requirements
- You enjoy analyzing complex problems and iterating quickly on experiments.
- LLM Familiarity: Experience with training or fine-tuning Large Language Models (LLMs) or familiarity with the modern NLP stack (Transformers, HuggingFace, etc.).
- Familiarity with hardware design concepts (Verilog, RTL, EDA tools), though not required.
Skills
- Previous internship experience at frontier AI labs or research organizations.
- Publications (or submissions) in top ML venues (NeurIPS, ICLR, ICML) or EDA venues (DAC, ICCAD).
- Competitive internship stipend
- Mentorship from a team of researchers and engineers from Anthropic, DeepMind, Meta, and Stanford
Benefits
- Opportunity to work on 0→1 problems in AI-driven chip design
- Responsible for co-designing and implementing the Reinforcement Learning experiments (GRPO/PPO/DPO), training data mixes and reward signal explorations.
- Education: Currently pursuing a PhD or Master's degree in Computer Science, Machine Learning, Mathematics, or a related field.
- RL Knowledge: Strong academic understanding or project experience with Reinforcement Learning (e.g., PPO, DPO, GRPO).
- Coding Proficiency: Strong proficiency in Python and deep learning frameworks (PyTorch).
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