Teams at PhysicsX

Owns the full lifecycle of task-based, in-platform technical documentation and how-to guides, from scoping and research through validation, publication, release updates, testing, and maintenance. Partners with curriculum leads, subject matter experts, Product, Engineering, and GTM leadership to ensure guides accurately support machine learning and simulation workflows. Maintains documentation backlogs, validates technical content independently, and keeps guidance current with platform releases.

Build and deliver complex FEA, multi-body, structural dynamics, and multi-physics models from geometry cleanup through post-processing and validation. Partner with customers and data science teams to create simulation datasets for AI and machine learning workflows. Use cloud and HPC resources to accelerate high-fidelity modeling, document technical work, and communicate engineering results. The role is hybrid in New York City and requires domestic and international customer travel for approximately 2–3 weeks per quarter.

Architect and build a production-grade agentic AI platform for engineering workflows. Responsibilities include agent observability, deployment, sandboxing, evaluation, governance, identity, access control, durable execution, agent memory, and developer experience. The role requires designing secure, reliable AI infrastructure using Python, Kubernetes, Docker, Terraform, agentic frameworks, vector databases, and evaluation tools.