Jobs at PhysicsX

Lead research workstreams applying machine learning and deep learning to physics and engineering problems. Set technical direction, plan roadmaps, mentor researchers, formulate mathematical models, build predictive models for physical systems, run experiments, collaborate with engineers and customers, translate research into production code, and communicate findings through publications and industry engagements.

Develop and productionize machine learning models, libraries, and tooling for physics and engineering applications. Build scalable cloud infrastructure for distributed multi-GPU and multi-node training and inference, optimize pipelines from scheduling through GPU kernels, process large-scale datasets, and apply software engineering best practices while collaborating with research scientists and simulation engineers.

Own the development and productionization of LLM post-training capabilities. Build data, training, evaluation, reward, verifier, and deployment pipelines; improve open models for engineering and agent benchmarks; manage customer-controlled model customization across SaaS, VPC, and air-gapped environments; and define the product roadmap in partnership with research, platform, and product teams.

Build and operate the LLM access layer for PhysicsX’s agent platform, including self-hosted model serving, routing, packaging, deployment, autoscaling, observability, performance optimization, and reliability. The role focuses on vLLM, SGLang, KServe, Kubernetes, GPU workloads, batching, caching, quantization, model parallelism, and air-gapped deployments. It also partners with research and post-training teams to move models into production and establish long-term serving architecture.

Develop simulation data-generation pipelines for Large Physics Models, using varied fidelity levels and commercial or open-source CFD tools. Build numerical and PDE solvers in differentiable or GPU-native frameworks such as JAX and NVIDIA Warp, assess fidelity-cost tradeoffs, and collaborate with ML researchers and engineers. The role contributes to research direction and requires postgraduate study, CFD industry experience, solver development, commercial CFD expertise, and comfort with open-ended research.

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.

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.

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 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.

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.

The Content & Communications Strategist will create compelling narratives for various channels, manage an editorial calendar, and collaborate across departments to enhance brand storytelling and engagement.

Lead deployment of production ML systems for physics-based engineering: design scalable training/inference pipelines, work with 3D point-clouds and mesh data, collaborate with customers onsite, mentor engineers, define architecture and success metrics, and deliver robust cloud and on-prem ML solutions.