Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Design, deploy, and maintain large-scale bare-metal GPU clusters for distributed training. Tune Slurm schedulers and Kubernetes integrations, optimize storage and high-speed networking, and build observability pipelines to ensure reliable, high-throughput GPU workloads for distributed deep learning.

Work on product-driven research to develop multimodal generative world models for image, video, and 3D. Design and train foundation models, implement post-training alignment (RLHF, DPO, reward models), and build large-scale data curation pipelines for sourcing, filtering, and labeling training data.

Design, optimize, and maintain large-scale distributed multi-GPU training systems. Diagnose numerical precision and stability issues (FP16/BF16/FP8), detect and recover from hardware/software faults, profile performance bottlenecks, and build developer tooling for resilient checkpointing and rapid fault recovery to maximize researcher productivity and hardware utilization.

Design, build, and operate large-scale crawling, ingestion, and ETL/ELT pipelines for petabyte-scale image and video datasets. Optimize distributed processing (Ray/Spark/Dask), ensure dataset provenance/versioning, reduce cost/latency, and collaborate with CV engineers and ML scientists. Provide technical leadership and mentor junior engineers.

Drive research and engineering of multimodal generative world models for image, video, and 3D. Design, train, and iterate foundation models, develop RLHF/DPO/reward-modeling alignment methods, build large-scale data curation pipelines, and create rigorous evaluation metrics for aesthetics, controllability, and correctness.