Software Engineer, Data Engineer

Physics

X is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software.

We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, Physics

X unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive.

Physics

X is developing a platform used by Data Scientists and Simulation Engineers to build, train, and deploy Deep Physics Models. As adoption grows, one of the most significant challenges is how data is accessed, shared, and governed across services, teams, and compute environments.

Simulation and AI workflows generate large and complex datasets — meshes, solver outputs, experiment artifacts, and derived model data — that must move seamlessly between interactive environments, automated pipelines, platform services, and external customer infrastructure.

Many engineering tools originate from POSIX file system assumptions, while modern cloud platforms are built around distributed services and object storage. Bridging these worlds requires preserving usability without inheriting the operational and scalability limitations of traditional mounting-based approaches.

Data permissioning is a core concern. This role will integrate fine‑grained authorisation into the platform’s data model to enable secure multi‑tenant data sharing.

We are looking for a Staff Software Engineer to lead the evolution of Data Integration Services — shared platform capabilities that define how data is discovered, accessed, exchanged, and governed across Physics

X.

This role focuses on designing cloud‑native data access patterns informed by file system semantics, distributed storage tradeoffs, and real‑world workload behaviour. You will help teams interact with data predictably regardless of where it resides, while ensuring the platform remains scalable, secure, and operable at scale.

• Define and drive the technical vision for Data Integration Services as a shared platform capability.
• Design scalable, cloud‑native data access patterns supporting simulation, ML, and platform workloads without tightly coupled mounting solutions.
• Establish consistent abstractions for accessing datasets across APIs, services, and compute environments.
• Design integrations with storage ecosystems including:
  • Object storage platforms (S3‑compatible systems, Azure Blob, GCS, MinIO)
  • Managed storage and file system abstraction platforms (e.g. FSx, Juice
    
    FS, Alluxio, or similar systems)
  • Protocol-based access layers such as WebDAV or HTTP‑based data interfaces
• Translate file system‑oriented workflows into reliable distributed service patterns where appropriate.
• Enable efficient data sharing across teams and tenants while maintaining strong isolation and governance controls.
• Design dataset lifecycle capabilities including provisioning, versioning, and retention strategies.
• Define how permissioning integrates into the data plane and is enforced consistently across services and workflows.
• Partner with platform identity and security teams to ensure consistent access enforcement.
• Define standards for data contracts and compatibility between services.
• Evaluate build‑vs‑buy decisions across storage, data access, and metadata tooling.
• Establish observability and performance practices for large‑scale data workflows.
• Mentor engineers and drive cross‑team adoption of shared data integration patterns.

• A passion for the craft — a drive for engineering excellence and a commitment to raising technical standards across teams.
• Strong software engineering foundations — algorithms, data structures, and system design, with a focus on building clean, maintainable, and testable systems. Strong command of Golang and Python
  .
• Distributed systems experience — proven track record designing and operating…