Member of Technical Staff - ML Research

About the role

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

• 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