Founding Member of Technical Staff - Post Training

What You’ll Do As a Founding Member of the Technical Staff (RL) at Architect, you’ll be at the forefront of post‑training the AI models for chip design tasks such as RTL code generation, verification, and architectural exploration. Co‑design and implement the Reinforcement Learning environments and algorithms, Reward Models training, and reward signal experiments. 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 theoretical performance translates into production‑ready implementations. 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. Qualifications & Skills PhD in Computer Science, 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. Deep expertise in reinforcement learning and post‑training, with a proven track record of taking models from research to real‑world deployment. Strong industry or research background building end‑to‑end ML pipelines. Experience with RL and fine‑tuning large language models and code models for reasoning, tool use, and structured coding tasks. Strong software engineering skills with experience building complex ML systems. Comfortable working with large‑scale distributed systems, high‑performance computing, and distributed training frameworks such as PyTorch, CUDA, QLoRA, and ZeRO. Adept at analyzing and debugging model training processes. Capable of balancing research exploration with engineering rigor and operational reliability. Fast‑moving builder who can prototype, benchmark, and productionize training pipelines with tight feedback loops. Bonus Worked on a post‑training team at frontier labs such as OpenAI, Anthropic, DeepMind, Mistral, MSL, Cohere, etc. Foundation in Electrical/Computer Engineering 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 deep‑tech 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 Architect