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- Contribute to the training and optimization of foundational generative models, including LLMs, diffusion models, and multimodal architectures (e.g., video, 3D, audio)
- Design scalable algorithms and training strategies for large-scale distributed training (multi-GPU, multi-node)
- Build core infrastructure, tools, and pipelines for data preprocessing, model training, evaluation, and deployment
- Explore and integrate cutting-edge techniques in model compression, parallelism, and efficient inference
- Collaborate across research, engineering, and product teams to align system design with product applications and deployment goals
- Stay up to date with state-of-the-art advancements in generative modeling, training infrastructure, and optimization — and publish when applicable
Salary : $200,000 – $350,000 + equity
Location : Bay Area
Responsibilities
- Contribute to the training and optimization of foundational generative models, including LLMs, diffusion models, and multimodal architectures (e.g., video, 3D, audio)
- Design scalable algorithms and training strategies for large-scale distributed training (multi-GPU, multi-node)
- Build core infrastructure, tools, and pipelines for data preprocessing, model training, evaluation, and deployment
- Explore and integrate cutting-edge techniques in model compression, parallelism, and efficient inference
- Collaborate across research, engineering, and product teams to align system design with product applications and deployment goals
- Stay up to date with state-of-the-art advancements in generative modeling, training infrastructure, and optimization — and publish when applicable
- Advanced degree in Computer Science, Machine Learning, or related field (or equivalent practical experience in training large models)
- Proficiency in Python and deep learning frameworks such as PyTorch or JAX
- Experience in at least one of the following areas:
- Large-scale model training and optimization (LLMs, diffusion, transformers, etc.)
- Scalable infrastructure for ML training (e.g., Ray, Kubernetes, distributed systems)
- Efficient data pipelines for training foundational models on multi-modal data (text, image, video, 3D)
- Systems-level contributions for compute, parallelism (e.g., FSDP, ZeRO, sharding), or inference efficiency
- Familiarity with evaluating generative models, including metrics, benchmarks, and human-in-the-loop evaluation
- Strong collaboration and communication skills; ability to work across disciplines and teams
- Publication record in top-tier ML or systems conferences (e.g., NeurIPS, ICML, ICLR, CVPR, SOSP, MLSys) is a plus
- Fully Covered Medical, Dental, and Vision Insurance
- Paid Time Off
- Parental Leave
- Free Lunches
- Team Building Events
- Collaborative Team environment
Accessibility Statement
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- Please let us know if you require any accessibility adjustments through the application or interview process.
Signify’s mission is to empower every person, regardless of their background
or circumstances, with an equitable chance to achieve the careers
they deserve. Building a diverse future, one placement at a time.
Seniority level
Seniority level
Mid-Senior level
Employment type
Employment type
Full-time
Job function
Job function
Engineering and Information TechnologyIndustries
Staffing and Recruiting
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