Jobs at PhysicsX

Own technical support issues from intake through resolution for enterprise customers and internal users. Investigate Kubernetes, Linux, container, Helm, cloud, monitoring, and GitOps problems; resolve issues or provide detailed escalations. Communicate clearly during incidents, guide customers remotely, write runbooks, improve support processes, and collaborate across Product, SRE, deployment, and engineering teams. The role supports SaaS, hosted/VPC, and air-gapped environments with planned evening shifts and weekend coverage.

Own the roadmap for PhysicsX’s Cloud Developer Environment and release engineering products while incubating an agentic software factory. Lead discovery, prioritization, secure software delivery, disconnected deployments, staged releases, rollback, installation experiences, and measurement of delivery quality and speed. Partner with engineering and deployment teams to productize developer tooling for regulated, customer-operated environments.

Develop and validate CFD models from geometry cleanup and meshing through simulation and post-processing. Support parametric CAD, simulation automation, design optimization, and creation of datasets for machine learning surrogate models. Run simulations using cloud and HPC resources, improve meshing and setup efficiency, communicate results to customers, and collaborate with data scientists and machine learning engineers.

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.

Design, operate, and optimize distributed training and serving infrastructure for large physics models on GPU/HPC platforms. Solve large-scale data I/O bottlenecks, build experiment tracking and observability, enable reproducible model packaging and deployment, and improve research developer experience while collaborating closely with research scientists and ML engineers.

Own customer relationships and deployment journeys for physics AI solutions. Translate engineering challenges into product opportunities, guide onboarding and change management, coordinate delivery teams, support roadmap and sprint planning, track adoption and value, and identify expansion opportunities. Prepare proposals and support commercial contracting while collaborating with simulation, software, machine learning, and data science teams.

Build and validate complex CFD and multiphysics models, including geometry cleanup, meshing, simulation, and post-processing. Develop parametric CAD models and automate simulation pipelines for optimization and design-of-experiments studies. Collaborate with data scientists, machine learning engineers, and customers to create simulation datasets and AI-enabled engineering solutions. Present recommendations, improve modeling practices, mentor junior colleagues, and work on-site with global customers for up to 2–3 weeks per quarter.

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.

Design, provision, and operate reliable shared infrastructure across AWS, GCP, Azure, and on-premises environments. Manage Kubernetes clusters, multi-tenancy, secrets, GPU workloads, networking, service meshes, and infrastructure as code using Terraform and Crossplane. Develop operators, establish reliability targets, respond to incidents, and partner with security and platform teams on governance and compliance.

Lead research on generative models and learned representations for 3D engineering geometry. Design and train models using representations, latent spaces, and physics-based objectives; evaluate outputs with production numerical solvers; formulate engineering problems with ML and simulation teams; guide research direction, mentor colleagues, and publish findings. The role focuses on geometric deep learning, generative modeling, neural fields, and coupling geometry generation with physical objectives.

Lead development and deployment of deep learning and probabilistic models for multi-physics engineering problems. Architect scalable data pipelines, collaborate with simulation engineers, drive R&D, mentor junior team members, and work directly with customers to integrate models into production, including onsite customer collaboration and presentations.

Own PhysicsX’s global events strategy and calendar, manage external conference programs, and produce executive, customer, research, talent, and launch experiences end-to-end. Responsibilities include budgeting, vendor and venue management, speaker coordination, event logistics, stakeholder collaboration, onsite execution, post-event analysis, and ROI reporting. The role builds scalable event processes and playbooks while ensuring experiences support commercial, research, talent, and brand objectives.

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.

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.