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We are looking for a highly motivated Product & Test Engineer to join our team and play a key role in the bring-up, test development, and productization of advanced IC products. This position involves close collaboration with design, operations, and manufacturing partners from early design stages through mass production.
Roles and responsibilities
Collaborate with design teams to review DFX architecture and define test requirements
Develop wafer sort and final test methodologies, including test content creation and program development
Perform test program coding, pattern conversion, and pre-silicon validation via virtual test simulations
Support hardware development: load boards, sockets, thermal solutions, and probe cards
Lead post-silicon test debug and sample delivery to internal and external stakeholders
Conduct test program characterization and tuning to improve coverage, yield, and quality
Work with OSATs and support manufacturing ramp-up and production
Contribute to quality and reliability test planning and execution

Were building foundational infrastructure to secure AI agents - including their identities, access patterns, and interactions with sensitive systems and data. This includes designing intelligent, dynamic mechanisms for ephemeral access control, secrets management, and agent/user identity tailored to modern agent frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, and beyond.
As the Product Owner, you will define and deliver the vision, roadmap, and execution of core platform capabilities. You will work closely with the R&D team, architecture, security, and stakeholders across the organization to ensure the platform aligns with technical and business needs, balancing deep engineering context with user impact.
This is a rare opportunity to help shape a new security infrastructure layer from the ground up - in a team with startup agility and the backing of a mature organization. Youll play a pivotal role in shaping how AI agents operate safely and effectively across real-world enterprise environments.
Responsibilities
Work closely with the Product Manager to understand customer needs, technical constraints, and business context deeply.
Translate high-level product requirements into clear, actionable epics, user stories, and functional specifications for the R&D team.
Create and maintain detailed functional documentation that guides engineering, architecture, and QA throughout development.
Own and manage the product backlog, ensuring clarity, prioritization, and alignment with roadmap objectives.
Collaborate daily with R&D to support sprint planning, refinement, and high-quality delivery.
Align with architecture and security teams to ensure product capabilities meet enterprise-grade security, scalability, and interoperability requirements.
Assist in defining and validating use cases related to agent identity, secrets access, delegation, and orchestration logic.
Foster continuous feedback loops between stakeholders and technical teams to ensure rapid iteration and value delivery.

We're looking for an AI Product Engineer to join the AI team and own how our product knowledge translates into reliable AI agent capabilities. This role sits at the intersection of product expertise and AI implementation. You won't write code or build ML models. Instead, you'll analyze agent interactions, create prompts and skills, improve documentation for AI consumption, label datasets, and write evaluations.
Think of this as a Knowledge and Content Manager for AI. You'll make sure our agents can accurately answer questions about us, guide users through workflows, and reduce support escalations.
Key Responsibilities:
Analyze AI inputs and outputs - Review agent conversations to identify gaps, hallucinations, and improvement opportunities
Create and maintain prompts and skills - Build reusable capabilities that encode our product logic and terminology
Improve documentation structure - Restructure docs so agents can cite accurate, step-by-step guidance
Label datasets and write evaluations - Measure agent quality across common questions and track improvements
Collaborate across teams - Work with Product, Docs, Support, and GTM to address real user pain points
Maintain the AI capability library - Keep prompts and skills organized with sendversioning and usage insights.