Founding ML Research Engineer RL for AI Chips (Equity)

About Architect Architect is a frontier AI lab for chip design. We build AI models and tools for on-demand custom ASICs at scale. Our goal is to co-design custom ASICs alongside evolving ML workloads, and enable a new era of domain-specific chips that unlock capabilities impossible with current hardware paradigms. Born out of Stanford Research, our team blends AI with Silicon with a founding team from Anthropic, Google DeepMind, Meta SuperIntelligence, xAI, Apple and Intel. What You’ll Do As a Founding 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, 01 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 #J-18808-Ljbffr