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The Research Engineer will enhance world models through multimodal data by building datasets, processing systems, and conducting experiments to improve model quality. Responsibilities include data acquisition, pipeline creation, and ensuring data's impact on model performance.

Lead full-stack development of user-centric web-based 3D graphics tools and backend generative AI services. Own features end-to-end, collaborate with research and design partners, ship responsive web apps, and iterate with early testers.

Build production-grade backend services and pipelines to turn 3D/ML research prototypes into reliable, debuggable features. Work across Python-based graphics/ML algorithms, FastAPI services, asset import/export integrations with DCC tools, and collaborate with frontend and research teams to ship reproducible, user-facing 3D workflows.

Design, implement, and optimize modern SLAM systems (visual, visual-inertial, lidar, multi-sensor). Build state estimation, sensor fusion, map management, loop closure, and benchmarking frameworks. Research hybrid learning-based SLAM, analyze failure modes, and productionize real-time, scalable mapping for downstream world-modeling applications.

Design, implement, and train large-scale diffusion models for generating 3D worlds. Drive end-to-end model development including data curation, experimentation, evaluation, deployment, and collaboration with research, engineering, and product teams. Mentor colleagues and integrate cutting-edge generative AI research into production-ready systems.

Lead technical vision and execution for 3D modeling and reconstruction, design state-of-the-art systems, mentor a small research/engineering team, drive end-to-end prototypes to production, and align capabilities with product needs while representing work externally.

Design, research, and productionize state-of-the-art 3D reconstruction systems (feed-forward and optimization-based). Develop representations, optimization and training pipelines, improve robustness and performance, collaborate with teams to integrate solutions into production, and drive technical direction and mentorship.

Develop and productionize robot learning systems and end-to-end policies for manipulation and locomotion. Build scalable training and evaluation pipelines emphasizing sim-to-real transfer, robustness, inference latency, and data efficiency. Collaborate with simulation, infrastructure, and systems teams, diagnose failures in sim and real-world rollouts, and drive research-to-production deployments.

The Research Platform Engineer will design and build critical infrastructure for AI research, optimize performance, and improve developer experience while collaborating with researchers.