Top Tech Jobs & Startup Jobs

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.

Own and evolve HPC infrastructure supporting CPU- and GPU-intensive engineering simulations, including cluster administration, scheduling, performance tuning, and capacity planning. Provide escalated IT support, administer identity and access controls, support developer tooling and cloud environments, and implement zero-trust and compliance practices. The role also requires strong networking, automation, scripting, troubleshooting, mentoring, and process-building skills in a scale-up environment.

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.

Build and own backend platform capabilities enabling data scientists, machine learning engineers, and simulation engineers to create, train, and deploy physics-informed models. Collaborate cross-functionally with backend, frontend, design, data science, and simulation teams. Develop scalable, observable production software using Python and Go, including testing, containerization, CI/CD, authentication, event-driven messaging, and Kubernetes-based applications.

Build and deploy customer-facing applications powered by physics AI models. Develop Python microservices and FastAPI REST APIs, prototype and iterate solutions with customers, collaborate with data scientists and machine learning engineers, and deploy cloud applications using Kubernetes, Helm, Terraform, AWS, and Azure. The role involves end-to-end application design, backend development, production deployment, and frequent international travel to customer sites.

The Senior ML Infrastructure Engineer will manage and optimize the ML infrastructure, focusing on training pipelines, model serving, and collaboration with research teams.