Jobs at PhysicsX

Designs, builds, and operates production AWS infrastructure for a multi-tenant cloud platform. Develops CI/CD pipelines, leads infrastructure architecture and code reviews, automates operational work, and creates self-service tooling. Requires deep AWS, Terraform, networking, IAM, observability, and data-store experience. Preferred qualifications include production Kubernetes, policy-as-code, compliance tooling, AWS GovCloud, and relevant AWS or Kubernetes certifications.

Own and evolve enterprise identity, access governance, zero-trust architecture, SASE, MDM, endpoint security, and infrastructure-as-code. The role designs Entra ID policies, MFA, PIM, SSO and SCIM integrations; manages mixed-OS device compliance and patching; replaces VPN access with modern SASE controls; automates identity workflows; and aligns enterprise and cloud security across AWS, GCP, and Azure. It also supports SOC 2, ISO 27001, FedRAMP, audits, access reviews, and least-privilege enforcement.

Develops predictive and deep learning models for engineering and physical systems, including data preprocessing, probabilistic modeling, optimization, and scalable production data pipelines. Collaborates with simulation engineers and customers to integrate AI models into simulations, support product development, and deliver practical solutions. The role includes customer presentations, onboarding, on-site collaboration, and travel to international customer sites.

Designs and scales DevSecOps practices by integrating security tooling into CI/CD pipelines, leading threat modeling and secure design reviews, managing product vulnerabilities, establishing secure coding standards, and mentoring engineering teams. The role focuses on web applications, APIs, cloud services, infrastructure, and customer security reviews while advancing the company’s security program.

Build and run CFD models from geometry cleanup and meshing through simulation and post-processing. Support parametric CAD, workflow automation, design optimization, and simulation dataset generation for machine learning models. Collaborate with data scientists, machine learning engineers, senior engineers, and customers to deliver practical engineering solutions. Use cloud and on-premise HPC resources, improve simulation performance, present results, and occasionally travel for customer engagements.

Build and deploy customer-facing applications that serve physics AI models. Collaborate with data scientists and ML engineers to design E2E solutions, build RESTful APIs and microservices (FastAPI/Python), deploy to cloud using Helm/Kubernetes/Terraform on AWS/Azure, and work onsite with customers to iterate and deliver production tools.

Lead development and validation of high- and low-frequency electromagnetic simulation models for semiconductor, automotive, aerospace, and other industries. Automate simulation pipelines, leverage cloud/HPC for high-fidelity runs, collaborate with data science/ML teams, present results to customers, mentor junior engineers, and travel to customer sites to deploy solutions and validate models with experimental data.

The Machine Learning Engineer will design and build scalable ML data pipelines, manipulate 3D data, collaborate with customers, and ensure reliable deployment of ML systems.

The Senior Machine Learning Engineer will design, deploy, and mentor on ML models, collaborate with customers, and influence product direction. Responsible for robust ML systems and data pipelines.

The Senior Forward Deployed Software Engineer will work with customers to build and deploy applications using physics AI models, collaborate with cross-functional teams, and maintain RESTful APIs and microservices in Python, focusing on impactful solutions for engineering challenges.