Senior / Staff Research Engineer, Simulation Assets & Content Systems

You will…

• Be part of a multidisciplinary team of Research Scientists and Engineers building the content backbone of a best-in-class multi-sensor simulation stack.

• Architect and own asset pipelines end-to-end: From raw sensor logs all the way into simulation-ready representations (e.g., 3DGS, NeRF, mesh, hybrid), validation, packaging, and serving into Waabi World.

• Design schemas, formats, and storage for a heterogeneous asset library spanning traditional mesh/material assets, neural representations, and generative-model outputs. Ownership of design decisions ranging from formats and compression, to streaming, and cross-renderer interop.

• Build tooling and visualization that lets research scientists, simulation engineers, and technical artists inspect, compare, debug, and curate assets at the scale of tens of thousands of items — including search, similarity, lineage, and regression review.

• Integrate cutting-edge research into the pipeline — collaborate with the neural rendering and generative AI teams to productionize the latest reconstruction, diffusion, and world-model techniques as first-class steps in the asset lifecycle.

• Collaborate with autonomy and safety teams to ensure the asset catalog meets the diversity, realism, and coverage demands of training and closed-loop evaluation.

Qualifications:

• Shipping Production Software. You have experience reading and developing production-quality software at scale, not just prototypes. You have shipped systems that other engineers and researchers depend on day-to-day.

• 3D Content Pipelines. You have built or extended large-scale 3D content pipelines — ingestion, processing, validation, versioning, and serving: for graphics, simulation, VFX, games, robotics, or AV. You understand the failure modes that emerge at tens of thousands of assets.

• Modern 3D Representations. You are fluent across at least several of: traditional mesh/material/PBR, OpenUSD, NeRF, 3D Gaussian Splatting, and procedural/generative content. You understand the trade-offs between paradigms — fidelity, edit-ability, runtime cost, storage, interop, and can make pragmatic choices.

• Cloud Infrastructure at Scale. Experience designing, launching, and debugging GPU jobs in the cloud (AWS, GCP, or equivalent), including job orchestration, cost modeling, and throughput optimization for batch and pipeline workloads.

• Strong software engineering fundamentals. You write efficient and maintainable code in Python, as well as in at least one native systems language such as C++ or Rust