Member of Technical Staff - Research Intern at Architect Labs

About Architect Labs

Architect is a frontier AI lab for custom silicon. We partner with frontier labs, clouds / neoclouds, physical AI companies, and advanced fabs to tape-out custom chips co-designed for next-generation AI workloads. Our goal is to compress end-to-end software to silicon timelines, and maximize intelligence per watt and per dollar for the world. We are a small exceptional team across silicon, systems, software and frontier AI. Our team have led research teams at nearly every frontier AI lab, and at some of the most complex SoCs in the world.

What You'll Do

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. This is a high-impact role where your experiments will directly influence our core modeling roadmap.

Responsible for co-designing and implementing the Reinforcement Learning experiments (GRPO/PPO/DPO), training data mixes and reward signal explorations.
Contribute to research on post-training techniques, running ablation studies to improve model reasoning and alignment capabilities.
Implement and test new algorithms for model fine-tuning and evaluation, helping to translate research papers into working prototypes.
Analyze experimental results and debug model behavior to help establish best practices for our training recipes.

What We'd Like to See

Qualifications & Skills:

Education: Currently pursuing a PhD or Master’s degree in Computer Science, Machine Learning, Mathematics, or a related field. Exceptional undergraduates with strong research experience are also encouraged to apply.
RL Knowledge: Strong academic understanding or project experience with Reinforcement Learning (e.g., PPO, DPO, GRPO). You should be comfortable reading and implementing concepts from recent research papers.
Coding Proficiency: Strong proficiency in Python and deep learning frameworks (PyTorch). You should be able to write clean, efficient research code.
Research Mindset: A fast learner who is comfortable navigating ambiguity. 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.).

Bonus:

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).
Familiarity with hardware design concepts (Verilog, RTL, EDA tools), though not required.

What We Offer

Competitive internship stipend
Mentorship from a team of researchers and engineers from Anthropic, DeepMind, Meta, and Stanford
Opportunity to work on 0→1 problems in AI-driven chip design

About Architect Labs

What You'll Do

Responsible for co-designing and implementing the Reinforcement Learning experiments (GRPO/PPO/DPO), training data mixes and reward signal explorations.

Contribute to research on post-training techniques, running ablation studies to improve model reasoning and alignment capabilities.

Implement and test new algorithms for model fine-tuning and evaluation, helping to translate research papers into working prototypes.

Analyze experimental results and debug model behavior to help establish best practices for our training recipes.

What We'd Like to See

Qualifications & Skills:

Education: Currently pursuing a PhD or Master’s degree in Computer Science, Machine Learning, Mathematics, or a related field. Exceptional undergraduates with strong research experience are also encouraged to apply.

RL Knowledge: Strong academic understanding or project experience with Reinforcement Learning (e.g., PPO, DPO, GRPO). You should be comfortable reading and implementing concepts from recent research papers.

Coding Proficiency: Strong proficiency in Python and deep learning frameworks (PyTorch). You should be able to write clean, efficient research code.

Research Mindset: A fast learner who is comfortable navigating ambiguity. 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.).