Applied Research Engineer – Video Data & ML @ Turing

About Turing Based in Palo Alto, California, Turing is one of the world's fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilingualism, STEM and frontier knowledge; and leveraging that expertise to build real-world AI systems that solve mission-critical priorities for Fortune 500 companies and government institutions. Turing has received numerous awards, including Forbes's "One of America's Best Startup Employers," #1 on The Information's annual list of "Most Promising B2B Companies," and Fast Company's annual list of the "World's Most Innovative Companies." Turing's leadership team includes AI technologists from industry giants Meta, Google, Microsoft, Apple, Amazon, Twitter, McKinsey, Bain, Stanford, Caltech, and MIT. For more information on Turing, visit www.turing.com. For information on upcoming Turing AGI Icons events, visit go.turing.com/agi-icons.Role Overview We are seeking an Applied Research Engineer with a strong foundation in video understanding, machine learning, or computer vision to help improve the quality of video datasets powering state-of-the-art AI models. This role is perfect for a candidate with 3–5 years of experience in ML/AI who is eager to deepen their skills through hands-on dataset development and small-model fine-tuning under the mentorship of senior engineers and researchers. You’ll work with ML teams, QA leads, and delivery managers to design precise, benchmark-aligned video annotation pipelines, contribute to small-scale model experiments, and enhance labeling workflows that directly support real-world AI systems. Strong cross-functional communication will be key to translating modeling goals into actionable annotation strategies. Key Responsibilities ML-Aligned Data Development Co-develop clear, structured guidelines for video annotation tasks including: Frame-level and segment-level classification Temporal localization and gesture/action recognition Multi-object tracking across frames and scenes Human-object and multi-agent interaction labeling Work with ML stakeholders…

Apply Now