Member of Technical Staff - Research Scientist

Architect Labs•Palo Alto, CA

About The Position

As a Member of the Technical Staff at Architect Labs, you will work at the intersection of chip design and frontier AI, translating hardware engineering expertise into agentic systems that can reason about, generate, and verify real silicon. The role focuses on researching self-improving harnesses that enable swarms of agents to work autonomously for days or weeks, and staying current with frontier research. You will work closely with ML research, post-training, and infrastructure teams to turn hardware expertise into reward signals, benchmarks, and training signals. You will build scalable software stacks, evaluations, and environments grounded in real hardware engineering work. In a fast-moving, early-stage environment, you will prototype, test, iterate, and turn ambiguous chip-design challenges into agent capabilities that ship.

Requirements

PhD in Computer Science or a related field, or at least 3 years of research experience at a frontier AI lab.
Experience designing and improving agent harnesses, including orchestration, tool use, memory, feedback loops, and recovery mechanisms for complex, long-horizon autonomous tasks.
Ability to stay current with frontier AI research, assess new approaches critically, and turn promising findings into measurable improvements.
Exceptional systems engineering skills, including architecting and shipping reliable, large-scale infrastructure and maintaining production standards for research code.
Ability to own ambiguous problems end to end, prototype quickly, and turn promising ideas into production systems.
Interest in applying frontier AI to chip design and willingness to learn from hardware engineers.

Nice To Haves

Research published at leading conferences such as ICLR, ICML, or NeurIPS.
Meaningful open-source contributions to coding agents, agent harnesses, or ML tooling, or experience building a harness from scratch.
Experience designing and deploying production multi-agent workflows and agents that run autonomously for days or weeks.
Experience building evaluations and benchmarks for complex engineering tasks.
Familiarity with development reward signals, post-training, or reinforcement learning methods for language models.
Familiarity with training or serving transformer-based models and building high-performance, large-scale ML systems.

Responsibilities

Research self-improving harnesses that guide swarms of agents working autonomously for days or weeks.
Stay current with state-of-the-art frontier AI research.
Partner with ML research, post-training, and infrastructure teams to turn hardware expertise into reward signals, benchmarks, and training signals.
Build scalable and robust software stacks that take ideas into production systems.
Build evaluations and environments for chip-design tasks that ground model behavior in real hardware engineering work.
Prototype, test, and iterate quickly, translating ambiguous chip-design challenges into concrete agent capabilities.