Founding Member of Technical Staff - ML Infra at Architect · Palo Alto

About Architect

Architect is a frontier AI research and product lab for chip design. We build AI models and systems that can explore, design, optimize, and verify new hardware. Our goal is to reimagine chip design using AI, cut down ASIC design time and cost, and enable a new era of ultra-efficient, domain-specific chips powering the future of computation.

Born out of Stanford, our team blends researchers and engineers from Anthropic, Google DeepMind, NVIDIA, Meta SuperIntelligence Labs, Apple, and Intel. Backed by leading VCs and angels, including the Chief Scientist at Google, Stanford professors, and founders of chip companies, Architect currently operates in stealth, pushing the limits of AI4EDA and building the intelligence layer for the hardware revolution.

What You'll Do

As a Founding Member of the Technical Staff (ML infra) at Architect, you'll be responsible for the critical algorithms and infrastructure that our researchers depend on to train models. Your work will directly enable breakthroughs in AI capabilities in chip designs.

• Build, maintain, and improve the algorithms and engineering systems used to post-train models for chip designs, focusing obsessively on improving training speed and reliability
• Profile reinforcement learning and training pipelines to detect bottlenecks and implement optimizations for high-performance training setups .
• Collaborate closely with ML researchers to implement stable and fast versions of new finetuning recipes (like in RLHF/SFT) on different model architectures.

What We'd Like to See

Qualifications & Skills:

• Degree: PhD in Computer Science, EECS, Mathematics, or a closely related field. Preferably, specialization in Machine Learning, Systems, or Artificial Intelligence. Or BS/MS with a strong research engineering background from frontier labs.
• ML Infrastructure: Proven track record of building end-to-end ML pipelines, including data curation, preparation, and large-scale LLM finetuning (RLHF, SFT).
• Debugging & Optimization: Adept at diagnosing why training runs slow down, building instrumentation to monitor system health, and fixing complex issues in distributed environments.
• Execution: Results-oriented with a bias towards flexibility and impact. You pick up slack and enjoy pair programming.

Bonus:

• Experience with implementing LLM finetuning algorithms (such as RLHF) and modifying systems based on model architectures.
• Worked on the post-training or infra team at frontier labs like 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 Systems (OSDI, SOSP) venues.
• Founding Engineer 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