Jobs at PhysicsX

Lead the design, deployment, and operation of production machine-learning systems for manufacturing and MRO. Build scalable training and inference pipelines for sparse, drifting industrial sensor data; integrate simulation with shop-floor measurements; establish uncertainty quantification and validation standards; and deliver reliable models across cloud, on-premises, and edge environments. Partner with data scientists, simulation engineers, manufacturing experts, and customers, define product requirements, lead technical architecture, mentor teams, and travel frequently to customer sites.

Own customer relationships and delivery for AI-enabled engineering solutions. Responsibilities include onboarding, change management, adoption tracking, executive reviews, account reporting, delivery coordination, customer feedback, and supporting business development and contracting. The role partners with simulation, software, machine learning, and data science teams to translate industrial challenges into practical solutions and product improvements.

Own day-to-day US People Operations, including onboarding, offboarding, employment documentation, payroll data, benefits, immigration processes, compliance, HRIS data, reporting, and performance cycles. Manage US benefits and broker relationships, maintain employment policies and I-9 records, support employee relocations, and contribute to broader People Ops projects. The role requires autonomy, discretion, strong organization, and comfort building processes in a rapidly growing company.

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.

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 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 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.

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.