What are the responsibilities and job description for the Founding Member of Technical Staff - ML Infra position at Architect?
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.
Qualifications & Skills
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.
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.
- 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.
- Competitive salary and meaningful equity stake
- Fast-paced startup with autonomy and visible impact
- Cutting-edge AI-driven chip design challenges