Architect Research Scientist at Architect Labs working at the intersection of chip design and frontier AI to develop agentic systems for silicon design. Focus on building scalable software stacks and autonomous agent harnesses.
Responsibilities
As a Member of the Technical Staff at Architect Labs, you'll sit at the intersection of chip design and frontier AI translating deep hardware engineering expertise into agentic systems that can reason about, generate, and verify real silicon.
Research on self-improving harnesses to guide a swarm of agents work autonomously for days / weeks. Stay on top of SOTA frontier research.
Partner closely with the ML research, post-training, and infra teams to turn hardware domain expertise into reward signals, benchmarks, and training signals.
Build scalable and robust software stacks that transform ideas into production systems seamlessly.
Build evals and environments that tackle chip-design tasks, grounding model behavior in real hardware engineering work.
Move fast in a 0 to 1 environment: prototype, dogfood, break things, iterate. Translate ambiguous chip-design challenges into concrete agent capabilities that ship.
What We'd Like to See
Qualification
BackgroundAgent Harness ResearchResearch InstinctSoftware EngineeringBuilder MindsetInterest in Hardware
Required
Background: PhD in Computer Science or a related field, or 3+ years of research experience at a frontier AI lab.
Agent Harness Research: Experience designing and improving agent harnesses, including orchestration, tool use, memory, feedback loops, and recovery mechanisms that enable agents to work autonomously on complex, long-horizon tasks.
Research Instinct: Ability to stay current with frontier AI research, critically assess new approaches, and turn promising findings into measurable improvements.
Software Engineering: Exceptional systems engineering. You architect and ship reliable, large-scale infrastructure that others build on, and you hold research code to production standards.
Builder Mindset: Ability to own ambiguous problems end to end, prototype quickly, and turn promising ideas into production systems. Pragmatic, resourceful, and comfortable moving fast.
Interest in Hardware: Excitement about applying frontier AI to chip design and a willingness to learn deeply from hardware engineers.
Strong candidates may also have experience with:
Research published at leading top conferences (ICLR, ICML, NeurIPS, etc).
Meaningful open-source contributions to coding agents, agent harnesses, or ML tooling, or building a harness from scratch.
Designing and deploying production multi-agent workflows and agents that run autonomously for days or weeks.
Building evaluations and benchmarks for complex engineering tasks.
Familiar with development reward signals, post-training or reinforcement learning methods for language models.
Preferred
Research published at leading conferences (ICLR, ICML, NeurIPS, etc)
Meaningful open-source contributions to coding agents or ML tooling
Experience designing and deploying production multi-agent workflows
Building evaluations and benchmarks for agent systems
