Job description
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:
- 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
Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. 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. You may be just the right candidate for this or other roles.
For applicants from the European Union, please review Turing’s GDPR notice here.
