Member of Technical Staff, ML and Data Infrastructure

About Hextrion

Hextrion is developing next-generation semiconductor advanced packaging to dramatically reduce the energy use, cost and physical footprint of future AI compute. As transistor scaling reaches its physical limits, advanced packaging has become the primary driver of AI performance, determining how efficiently data and power move to and between chips.

We are led by former C-suite and senior executives from the global advanced packaging leaders serving NVIDIA, AMD, Broadcom, Google, Amazon and Microsoft. They are joined by physics AI researchers from Harvard and industry leaders who have deployed production AI systems at TSMC and Apple component manufacturers. We combine deep packaging expertise with physics AI agents and reinforcement learning to design and manufacture advanced packaging with greater performance, precision and yield than current methods deliver.

About the role

As a Member of Technical Staff on ML and Data Infrastructure, you'll design and build the data and compute foundation for AI that operates in both the digital and physical world. You'll own the systems that turn design, manufacturing, metrology and large-scale simulation data into training-ready datasets, and the platform that trains, deploys and monitors our physics AI models and agents across design and manufacturing.

What you'll do

• Architect and build scalable data pipelines that integrate design, manufacturing, inspection, metrology and simulation data into a unified, versioned platform.
• Build and run our ML platform: compute, training pipelines, experiment tracking and model management.
• Build large-scale training infrastructure for physics AI models and agents, including distributed training across GPU clusters.
• Run high-throughput simulation and data-generation workloads that create training data at scale.
• Deploy, serve and monitor models in production, including near the manufacturing line.
• Integrate models and agents into engineering design workflows and tools, including electronic design automation (EDA) environments.
• Design secure infrastructure for sensitive data, with strong access controls and auditability.

What we're looking for

• Experience building data or ML infrastructure at a leading AI lab or large-scale tech company, or equivalent impact elsewhere.
• Deep experience with distributed systems, large-scale data pipelines and GPU training infrastructure.
• Strong engineering skills in Python and modern infrastructure (Kubernetes, cloud and on-premises clusters, orchestration and MLOps tooling).
• A track record of owning critical systems end to end and shipping them reliably at scale.
• A plus: experience with scientific, simulation, design (EDA), manufacturing or industrial data; edge deployment; high-performance computing; or building LLM applications and agent tooling.

Role Details

• Minimum education: Bachelor's degree.
• Compensation: $180,000–$350,000 + equity, depending on experience
• Visa sponsorship: Yes. We will support the visa process.