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

Ellis Technologies, Inc.; San Jose (CA); On-site; USD 156,000 - 316,800; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) savings plan with company match

Paid parental leave

Short-term and long-term disability coverage

Life insurance

Paid time off

Job summary

Ellis Technologies, Inc. is seeking a Research Scientist specializing in privacy-preserving model training and architecture optimization in San Jose. The candidate will design and optimize large-scale training architectures for advanced generative models and lead performance optimization efforts across GPUs.

This role requires collaboration with research teams and entails significant responsibilities in developing robust, fault-tolerant systems. A strong background in large-scale deep learning and expertise in GPU optimization is critical, along with proficiency in PyTorch.

Competitive benefits include medical, dental, vision insurance, a 401(k) plan with company match, and more, ensuring employee wellbeing and satisfaction from day one.

Qualifications

• Strong background in large-scale deep learning systems and distributed training.
• Hands-on experience with GPU optimization including memory management.
• Experience training diffusion models at scale.
• Proficiency in PyTorch and modern distributed training stacks.
• Solid understanding of parallelism strategies.

Responsibilities

• Design and optimize large-scale training architectures for generative models.
• Lead GPU-centric performance optimization for large training systems.
• Develop and evolve distributed training strategies tailored to long-running jobs.
• Build fault-tolerant, self-healing training systems.
• Design mechanisms for fast failure detection and recovery.

Skills

Large-scale deep learning systems

GPU optimization

Distributed training

PyTorch

Parallelism strategies

Job description

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.