AI Research Engineer (Pre-training - LLM & Multi-Modal)
Drive innovation in AI model architecture development, focusing on Large Language Models and Multi-Modal systems to enhance intelligence and efficiency.
60 total jobs
6 new jobs this week
Drive innovation in AI model architecture development, focusing on Large Language Models and Multi-Modal systems to enhance intelligence and efficiency.
Drive innovation in AI architecture development by enhancing Large Language Models and Multi-Modal systems through cutting-edge techniques and optimization strategies.
Drive innovation in AI model architecture development, focusing on large-scale pre-training for LLMs and multi-modal systems to enhance intelligence and efficiency.
Drive innovation in AI model architecture development, focusing on large-scale pre-training for LLMs and multi-modal systems to enhance intelligence and efficiency.
Drive innovation in AI architecture development by enhancing Large Language Models and Multi-Modal systems, focusing on pre-training optimization and groundbreaking advancements.
Drive innovation in AI model architecture development, focusing on large-scale pre-training and multi-modal systems to enhance intelligence and efficiency.
Drive innovation in architecture development for cutting-edge AI models, enhancing intelligence and efficiency through pre-training and novel techniques.
We're looking for a Principal Research Engineer (L7) to own the full technical stack for offline video generation. This is a senior individual contributor position with outsized scope and influence.
Drive innovation in AI model serving and inference architectures, optimizing deployment strategies for efficient and scalable performance across diverse applications.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while preserving accuracy.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while preserving accuracy.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while maintaining accuracy.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while maintaining accuracy.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while preserving accuracy.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while preserving accuracy.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while preserving accuracy.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while preserving accuracy.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while preserving accuracy.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while preserving accuracy.
Drive innovation in training and optimizing vision-language models, focusing on real-world deployment and enhancing model quality and efficiency.
Drive innovation in multi-modal reinforcement learning to enhance intelligent systems, optimizing decision-making across text, images, and audio for real-world challenges.
Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors and tool use to enhance intelligence and autonomous capabilities.
Conduct vulnerability research and software security assessments, applying debugging, systems programming, and low-level development skills while gaining experience across cybersecurity projects.
As a Research Engineer on the RL Velocity team, you'll enhance the efficiency and reliability of the RL Science stack, enabling researchers to iterate quickly on training runs.