AI Jobs

This role involves developing novel machine learning methods and advancing OpenAI's research agenda across domains like multimodal learning, reasoning, robotics, and alignment. You will conduct original research to advance AI state-of-the-art, design and evaluate novel algorithms at scale, and collaborate with interdisciplinary teams to translate research into production systems.

This role involves designing and implementing advanced machine learning solutions with a focus on generative AI models, LLMs, and computer vision at scale. Required expertise includes 7+ years in data science and machine learning with proven experience in building production-grade AI systems.

This role involves conducting deep learning research with a focus on Reinforcement Learning and Large Language Models at Hewlett Packard Labs. The position requires extensive experience in machine learning research and contributions to cutting-edge AI development.

Leads frontier AI research projects at Georgetown's Center for Security and Emerging Technology, coordinating research teams and briefing policymakers on AI and national security policy. Requires expertise in artificial intelligence, machine learning, data analysis, and the ability to design research plans, manage teams, and produce rigorous publications.

This role focuses on advancing reasoning capabilities in large language models (LLMs) and agentic systems, with emphasis on identifying optimal data types and methodologies for improving LLM-based agents including browser and software engineering agents. The ideal candidate needs deep expertise in LLMs, planning algorithms, agentic reasoning, and creative problem-solving in data generation, model interaction, and evaluation.

Build distributed training, inference, and RL infrastructure systems that power Cursor's frontier coding models and scale them on real user data. This role requires strong distributed systems expertise, end-to-end ownership, and deep intuitions about how language models work.

This role involves evolving Netflix's core measurement and analytics tools by integrating cutting-edge scientific advances in pricing and measurement methodologies. The position requires expertise in experimental design, quantitative research, and machine learning frameworks like TensorFlow to drive data-driven pricing strategies.

This role focuses on developing and researching machine learning algorithms and models for quantitative trading and financial applications at Susquehanna International Group. The position requires strong foundations in machine learning theory, mathematical optimization, and programming expertise.

Build, profile, and optimize Scale's internal distributed framework (RLXF) for large language model training and inference that powers MLEs, researchers, and data scientists across the organization. This role requires strong system optimization expertise, experience with multi-node LLM training/inference, and proficiency in developing large-scale distributed ML systems.

This role focuses on developing novel post-training techniques including SFT, RLHF, and reward modeling to enhance large language model capabilities across text and multimodal modalities. Required expertise includes deep learning, reinforcement learning, and experience with large-scale generative models, with expectations to publish research at top-tier AI conferences.

This role bridges cutting-edge AI research and practical application within Scale's Agent Capabilities & Environments team, focusing on autonomous agents that interact with diverse external environments including code repositories, GUI interfaces, and browsers. Required expertise includes machine learning research, agent systems, reinforcement learning, LLM capabilities, and benchmarking frameworks for evaluating model performance.

Lead the technical strategy and development of next-generation deep research agents for enterprise applications, translating cutting-edge AI research into production systems. This role combines hands-on machine learning engineering with strategic technical leadership, focusing on LLMs, agentic frameworks, and advanced knowledge retrieval systems.

This fellowship role involves collaborating with top researchers to design, evaluate, and optimize advanced generative AI systems, including designing and reviewing PyTorch models, evaluating ML code efficiency, and advising on GPU optimization. Key skills required include deep learning expertise, PyTorch proficiency, ML model optimization, and the ability to evaluate complex AI-generated implementations for correctness and performance.

Lead a team of research scientists and engineers to develop novel evaluation methodologies, metrics, and benchmarks for assessing large language model capabilities and limitations. Design cutting-edge LLM evaluation techniques and conduct research on effectiveness of existing evaluation approaches to advance generative AI development.

Lead the development of novel evaluation methodologies, metrics, and benchmarks to measure the capabilities and limitations of frontier large language models. Drive research on LLM evaluation effectiveness, design innovative benchmarks for instruction following and factuality, and define best practices in data-driven AI development.

This fellowship role involves collaborating on high-impact AI research projects, designing and evaluating advanced generative AI systems, and optimizing PyTorch models for real-world deep learning workflows. Key skills include proficiency in PyTorch, deep learning expertise, GPU optimization knowledge, and ability to evaluate ML code for efficiency and correctness.