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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.

PhysicsX invites candidates to join its talent community for future opportunities across research, data science, machine learning engineering, product, and delivery. The company develops AI-driven simulation software for engineering and manufacturing in advanced industries including aerospace, energy, semiconductors, materials, and automotive. Applications are reviewed for potential future roles, with no active vacancy or immediate progression guaranteed.

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.

The Machine Learning Engineer will design and implement scalable ML pipelines, work with 3D data, and collaborate directly with clients on engineering challenges, ensuring solutions are effective and impactful.

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.