We are seeking a highly skilled Research Engineer with extensive experience in training Generative AI models. As part of our research team, you will play a key role in building state-of-the-art multimodal foundation models and managing large-scale training runs on thousands of GPUs. Your expertise will directly impact the performance, scalability, and efficiency of our next-generation AI technologies.
Key Responsibilities Lead and contribute to groundbreaking research in multimodal foundation models. Design, develop, and experiment with innovative algorithms, architectures, and techniques to enhance model performance and scalability. Optimize models for production environments, focusing on computational efficiency, throughput, and latency while maintaining accuracy and robustness. Analyze and manage large-scale data clusters, identifying inefficiencies and bottlenecks in training pipelines and data loading processes. Collaborate with cross-functional teams, including data, applied research, and infrastructure teams, to drive impactful projects. Qualifications Technical Expertise:
Demonstrated strong engineering skills in Python and PyTorch. Hands-on experience building machine learning models from scratch using PyTorch. Familiarity with generative multimodal models such as Diffusion Models and GANs. Solid understanding of foundational deep learning concepts, including Transformers. Preferred Experience:
1 year+ industrial or academic lab experience. Experience working with large distributed systems involving 100+ GPUs. Proficiency with Linux clusters, systems, and scripting. Note: This role is open to recent graduates.
Compensation The salary range for this position in California is $160,000–$200,000 per year. The final offer will be based on job-related expertise, skills, candidate location, and experience. Additionally, we provide competitive equity packages in the form of stock options and a comprehensive benefits plan.