Member of Technical Staff ML Research

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.

As a Member of the Technical Staff at Architect youll 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 youll own the end-to-end RL workflowfrom 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:

• 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.

• 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.

• Competitive salary and meaningful equity stake

• Fast-paced startup with autonomy and visible impact

• Cutting-edge AI-driven chip design challenges

Required Experience:

Staff IC