Turing is seeking an Applied Research Engineer to improve the quality of video datasets that power state-of-the-art AI models, by designing precise, benchmark-aligned video annotation pipelines and contributing to small-scale model experiments.
Requirements
- 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.
Responsibilities
- 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.
- 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.
- 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.
- Collaborate with QA leads to build gold sets, spot-check protocols, and error rubrics that improve consistency and reduce ambiguity.
Other
- Strong cross-functional communication will be key to translating modeling goals into actionable annotation strategies.
- Act as a technical bridge between ML engineers, annotators, and QA reviewers.
- Create clear documentation and communicate updates across technical and non-technical stakeholders.
- Excellent communication skills—comfortable presenting ideas, gathering requirements, and collaborating across teams.
- Full-time remote opportunity
