We are looking for a software engineer with strong backend skills, a product mindset, and the curiosity to bring LLM-powered agents into the toolchains and Linux environments where chips are actually designed. Success in this role means owning the agentic infrastructure end-to-end: from the platform our agents run on, to the integrations that let them safely act inside our customers' existing flows.
Description
You will join a growing team of ML and software engineers building GenAI solutions for chip design teams. In this role, you will:
* Architect, build, and operate the agentic infrastructure our team relies on:including LLM orchestration, tool-calling layers, multi-agent workflows, and the services that host them.
* Design and implement software solutions for chip design engineers, translating their workflows into reliable, automated agent-driven systems.
* Integrate our agents and services with existing Linux-based environments, in-house tools developed by the team, and third-party EDA tools.
* Manage agentic and ML workflows in production to ensure performance, high availability, and resource efficiency.
* Deploy and maintain production services, with proactive monitoring to catch quality and infrastructure regressions before they reach users.
* Build automated evaluation frameworks to measure retrieval quality, agent execution, and LLM output accuracy across releases.
* Partner with CAD teams to integrate pipelines and agents into CI/CD, ensuring reliable deployments and safe rollbacks.
* Implement access controls, authentication, and secret-handling practices appropriate for working with sensitive design data.
* Collaborate closely with ML engineers, researchers, and our customer teams.
Requirements: 5+ years of full stack development experience, working with relational and NoSQL databases (e.g., MongoDB, SQL).
Strong object-oriented programming and system design skills, with proficiency in both TypeScript and Python across frontend and backend development.
Hands-on experience building dynamic web applications utilizing React for the frontend and Node.js or Python for the backend.
Familiarity with microservices, containerization (Docker), orchestration (e.g., Kubernetes), and CI/CD practices (e.g., GitLab, GitHub Actions, Jenkins).
Proven experience designing, developing, and scaling real-time and distributed systems.
Hands-on experience deploying LLM-powered applications, agents, or related components (e.g., vector databases, RAG pipelines) in production.
Excellent communication and collaboration skills, with the ability to bridge engineering teams and explain technical trade-offs clearly.
This position is open to all candidates.