Video Research Lead

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 to align labeling specs with downstream use cases such as action classification, event detection, and object tracking.
  
  

Benchmark-Driven Data Optimization

• Identify labeling gaps affecting model performance on public benchmarks (e.g., MVBench, LongVideoBench, Video-MME, AVA-Bench).
  
  
• Recommend guideline updates based on error analysis and metric improvements.
  
  

Model Collaboration & Fine-Tuning

• Support small-scale model fine-tuning efforts (e.g., vision transformers or temporal CNNs) under the guidance of senior engineers.
  
  
• Run basic evaluation experiments to assess annotation quality and model impact.
  
  

QA and Labeling Process Support

• Collaborate with QA leads to build gold sets, spot-check protocols, and error rubrics that improve consistency and reduce ambiguity.
  
  
• Help close the loop on annotation feedback through structured escalation and review.
  
  

Cross-Functional Communication

• Act as a technical bridge between ML engineers, annotators, and QA reviewers.
  
  
• Create clear documentation and communicate updates across technical and non-technical stakeholders.
  
  

Qualifications

• 3–5 years of experience in computer vision, applied ML, or data-centric AI, especially involving video data or temporal modeling.
  
  
• Working knowledge of video modeling techniques and benchmarks for tasks like tracking, segmentation, or action recognition.
  
  
• Some hands-on experience with fine-tuning or evaluating small ML models using tools like PyTorch, TensorFlow, or Hugging Face.
  
  
• Familiarity with video labeling tools (e.g., CVAT, VOTT, Labelbox, SuperAnnotate) or experience working with custom platforms.
  
  
• Strong understanding of the ML data lifecycle, including synthetic data, annotation QA, and human-in-the-loop systems.
  
  
• Ability to interpret relevant research papers and translate them into actionable annotation or modeling improvements.
  
  
• Excellent communication skills—comfortable presenting ideas, gathering requirements, and collaborating across teams.
  
  

What Success Looks Like

• Scalable, reproducible annotation pipelines that improve model accuracy on benchmark tasks.
  
  
• Well-documented workflows that reduce ambiguity and inter-annotator error.
  
  
• Hands-on contributions to model fine-tuning and evaluation under mentorship.
  
  
• Constructive collaboration across ML, data, and QA teams that accelerates iteration and deployment readiness.
  
  

 

Advantages of joining Turing: