AI Jobs

This role involves building and maintaining full-stack features with a focus on backend systems for ElevenLabs' AI voice platform serving millions of users and enterprise clients. Required skills include backend development expertise, full-stack capabilities, and experience with scalable systems.

This role leads go-to-market enablement and partnership strategy for ElevenLabs, focusing on scaling revenue through strategic partnerships and partner enablement programs. The ideal candidate possesses strong GTM expertise, partnership development skills, and the ability to drive revenue growth across enterprise and startup segments.

This role focuses on building and managing strategic revenue partnership opportunities for ElevenLabs' AI voice and platform products. Key skills include partnership development, business development, sales acumen, and the ability to identify and negotiate mutually beneficial deals with enterprise clients and technology partners.

This role involves training AI models and providing analytics expertise as a contractor for DataAnnotation. Key skills include data analysis, machine learning model evaluation, and the ability to work independently on flexible schedules.

This role involves owning the AI product strategy and implementation for Salesforce solutions, with a focus on Sales Cloud and Service Cloud platforms. Key skills include B2B SaaS product management, Salesforce expertise, AI/ML product knowledge, and experience with Sales, Support, and Customer Success best practices.

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.

This role involves developing AI-enabled features and working with cross-functional teams to implement generative AI solutions. Required skills include Azure, Node.js, Git, Google Cloud Platform, and .NET, with a Bachelor's degree in Computer Science, Data Science, Mathematics, or related STEM field.

This role involves working directly with customers on high-impact data science and AI deployment problems, building production tools including evaluation explorers, operator workflows, and decision-support systems. The ideal candidate excels at first-principles problem solving, experimental design, rigorous measurement, and technical execution using Python, SQL, and modern AI-assisted development tools.

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.

Lead the infrastructure engineering efforts for Scale AI's enterprise platform, architecting multi-cloud systems and deployment standards for Agentic workflows across GCP, Azure, and AWS. Design scalable, secure infrastructure solutions while mentoring teams and defining long-term technical strategy for knowledge retrieval and inference engines.

Build and optimize systems powering Scale AI's Generative AI Data Engine, which produces critical datasets for training and evaluating the world's most advanced LLMs and generative models. You'll work across teams focused on RLHF, human data generation, model evaluation, safety, and alignment to shape how humanity interacts with AI.

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 high-impact strategic initiatives for Scale's Generative AI business unit, owning customer pipelines that drive $XXM+ in revenue while managing data quality, operational efficiency, and global teams. This role requires strong technical expertise in Machine Learning Engineering, Computer Science, or Software Engineering to design data solutions, build pipeline infrastructure, and partner across Engineering, Product, and Go-to-Market stakeholders.

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 role involves designing and executing solutions, tools, and processes that power high-quality training data creation for robotics AI models, ensuring precise and timely dataset delivery for perception and autonomous systems. The ideal candidate combines operational excellence, process improvement expertise, and cross-functional leadership skills to drive critical workflows and collaborate globally across engineering, marketing, analytics, and operations teams.