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 Member of the Technical Staff at Architect, you'll be at the forefront of training AI models for chip design, verification and exploration tasks. You will be doing fundamental research and applying that to industry-grade chips going into production at leading foundry technologies like TSMC.
Responsible for co-designing and implementing the Reinforcement Learning environments and algorithms, Reward Models trainings and reward signal experiments.
You will work at the intersection of cutting-edge research and production engineering for chip designs, implementing, scaling, and improving post-training techniques to enhance model capabilities and usability .
Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation, ensuring that theoretical performance translates into production-ready implementations.
This is a hands-on, 0→1 role where you'll own the end-to-end RL workflow—from reward modeling and environment design to test-time optimization and scaling.
Collaborate with research teams to translate emerging techniques into production-ready implementations and debug complex issues in training pipelines and model behavior.
What We'd Like to See
Qualifications & Skills:
Degree: PhD in Computer Science, Computer Engineering, EECS, Mathematics, or a closely related field. Preferably, specialization in Machine Learning, Deep Learning, or Artificial Intelligence. Or BS/MS with a strong research engineering background.
RL & Post-Training Expertise: Deep expertise in reinforcement learning and post-training, with a proven track record of taking models from research to real-world deployment.
Model Training: Strong industry or research background building end-to-end ML pipelines. Experience RL and fine-tuning LLMs and code models for reasoning, tool use, and structured coding tasks.
Systems Engineering: Strong software engineering skills with experience building complex ML systems. Comfortable working with large-scale distributed systems, high-performance computing, and distributed training frameworks (e.g., PyTorch, CUDA, QLoRA, ZeRO).
Engineering Rigor: Adept at analyzing and debugging model training processes. Capable of balancing research exploration with engineering rigor and operational reliability.
Execution: Fast-moving builder who can prototype, benchmark, and productionize training pipelines with tight feedback loops.
Bonus:
Worked on the post-training team at frontier labs like OpenAI, Anthropic, DeepMind, Mistral, MSL, Cohere, etc.
Foundation in Electrical/Computer Engineering, Computer Architecture, and chip-design or verification processes (not required, but a plus).
Publications in top ML (NeurIPS, ICLR, ICML) or EDA (DAC, ICCAD, DVCon) venues.
Experience as a Founding ML Engineer/Researcher or early hire at an AI deeptech startup.
What We Offer
Competitive salary and meaningful equity stake
Fast-paced startup with autonomy and visible impact
Cutting-edge AI-driven chip design challenges
