Member of Technical Staff - ML Research

Architect Labs; Palo Alto (CA); On-site; USD 190,000 - 260,000; Full time

Benefits offered by this job

Competitive salary

Meaningful equity stake

Job summary

Architect is a frontier AI lab focused on custom silicon and AI workloads. As a Member of the Technical Staff, you will help train AI models for chip design, verification and exploration, with productionization on leading foundry tech like TSMC.

You will co-design RL environments, implement reward models, and scale post-training techniques while collaborating across research and production teams to deliver reliable, high-performance systems.

Qualifications

• PhD or strong research-backed MS/BS in a relevant field.
• Deep RL expertise with deployment experience.
• End-to-end ML pipelines and model fine-tuning experience.
• Experience with large-scale distributed systems and HPC.
• Ability to balance research with engineering rigor and reliability.

Responsibilities

• Co-design and implement RL environments and reward models.
• Work at intersection of research and production for chip designs.
• Build and run robust pipelines for model fine-tuning and evaluation.
• Own end-to-end RL workflow: reward modeling to test-time optimization.
• Collaborate with researchers to translate techniques into production.

Skills

Reinforcement learning

Post-training ML

End-to-end ML pipelines

Distributed systems

PyTorch/CUDA

Model evaluation

Education

PhD in CS/EE or related

BS/MS with strong research background

Tools

PyTorch

CUDA

LoRA/QLoRA

Distributed training

Job description

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