Research Scientist Privacy-Preserving Large-Scale Model Training & Architecture Optimization

Research Scientist — Privacy-Preserving Large-Scale Model Training & Architecture Optimization Location: San Jose Employment Type: Regular Job Code: DW1L Responsibilities Design and optimize large-scale training architectures for diffusion-based and unified generative models (e.g., DiT, Rectified Flow, hybrid AR + diffusion systems). Lead GPU-centric performance optimization, including memory layout, communication overlap, kernel fusion, and throughput scaling across thousands of accelerators. Develop and evolve distributed training strategies (DP / TP / PP / ZeRO / FSDP-style sharding) tailored to long-running, multi-stage foundation model training. Build fault-tolerant, self-healing training systems that can sustain long-running jobs under frequent hardware, network, and software failures. Design mechanisms for fast failure detection, recovery, and minimal training interruption, including checkpointing strategies, restart policies, and controlled rollouts. Improve training ETTR / MFU / utilization efficiency under real-world production constraints. Optimize Diffusion Transformer training pipelines, including noise schedules, timestep strategies, and memory-efficient attention mechanisms. Support unified generation-and-understanding models, enabling shared context, long-sequence multimodal reasoning, and scalable training without architectural bottlenecks. Collaborate with research teams on architecture-level tradeoffs between quality, compute efficiency, and training stability. Qualifications Minimum Qualifications: Strong background in large-scale deep learning systems and distributed training. Hands‑on experience with GPU optimization, including memory management, communication/computation overlap, and performance profiling. Experience training diffusion models, DiT‑style architectures, or large foundation models at scale. Proficiency in PyTorch and modern distributed training stacks. Solid understanding of parallelism strategies (DP / TP / PP / ZeRO / FSDP or equivalents). Ability to reason about training stability, numerical issues, and long-running job robustness. Preferred Qualifications: Experience with privacy-preserving ML, sensitive data training, or regulated environments. Familiarity with fault-tolerant training systems, checkpointing strategies, or production GPU orchestration. Experience with unified multimodal models (generation + understanding) or hybrid AR/diffusion systems. Low-level performance work (CUDA kernels, custom ops, fused attention, or communication libraries). Background in production ML infrastructure supporting thousands of GPUs. Job Information The base salary range for this position in the selected city is $156,000 - $316,800 annually. Benefits Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, and more. Employees also receive 10 paid holidays per year, 10 paid sick days per year, and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure). The Company reserves the right to modify or change these benefit programs at any time, with or without notice. Employment Eligibility Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues; Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; Exercising sound judgment. #J-18808-Ljbffr