Turing is seeking an Applied Research Engineer to improve the quality of video datasets used to train state-of-the-art AI models, specifically focusing on video understanding, machine learning, and computer vision.
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, and 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.
- Amazing work culture (Super collaborative & supportive work environment; 5 days a week)
- Awesome colleagues (Surround yourself with top talent from Meta, Google, LinkedIn etc. as well as people with deep startup experience)
- Competitive compensation
- Flexible working hours
- Full-time remote opportunity
- Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics.
- At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways.
- For applicants from the European Union, please review Turing's GDPR notice here
