Senior Deep Learning Engineer, Cosmo 3D Spatial

Responsibilities

• Own the 3D data engine for Cosmos spatial reasoning: source, curate, filter, and balance large-scale real-world image and video corpora into vision-language training data with the coverage and diversity that spatial understanding demands.
• Build the annotation and auto-labeling pipelines that produce 3D-grounded supervision at scale, camera-relative 3D boxes, referring and spatial question answering, free space and reachability, ego-, world-, and object-centric reference frames, cross-view correspondence, camera motion, distance and size, and…
• Own data quality end to end: semantic deduplication, automated quality scoring for faithfulness, completeness, and correctness, coverage analysis across scene types and reference frames, and the sampling strategies that keep pre-training and supervised fine-tuning mixtures balanced.
• Verify end-to-end model training: run and validate full pre-training and supervised fine-tuning pipelines, guard reproducibility, catch data and checkpoint regressions, diagnose throughput and loss anomalies, and attribute capability changes back to the specific data and recipe decisions that caused them.
• Build and operate the 3D and spatial evaluation suite, public benchmarks such as CV-Bench, BLINK, RefSpatial, VSI-Bench, SPAR-Bench, and RoboSpatial, NVIDIA's VANTAGE-Bench for real-world fixed-camera video understanding, and in-house benchmarks you design with continuous evaluation and full traceability from every…
• Partner closely with Cosmos Lab research scientists: translate 3D research hypotheses into dataset and ablation experiments, run them at scale, and feed honest results back into recipe and architecture decisions.
• Operate on large multi-node GPU clusters, tuning data throughput, sharding, and dataloader performance so that data is never the bottleneck on a long training run.
• Ship the results into Cosmos releases, open-source datasets and benchmarks where appropriate, and raise the bar for data and evaluation rigor across the team.
• What we need to see
• MS or PhD in Computer Science, Electrical/Computer Engineering, Robotics, or a related field, or equivalent experience.

Languages