Turing is hiring: Applied Research Engineer - Video Data & ML in San Francisco

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Join to apply for the Applied Research Engineer - Video Data & ML role at Turing

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This range is provided by Turing. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$170,000.00/yr - $200,000.00/yr

Additional compensation types

RSUs

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Senior Talent Acquisition Partner | Leadership, Technical, G&A Recruiting

Overview

We are seeking an Applied Research Engineer with a strong foundation in video generation, machine learning, or computer vision to help improve the quality of datasets and workflows powering next-generation video synthesis models. This role is ideal for a candidate with 3–5 years of experience in ML/AI who is eager to grow their expertise through hands-on data development and small-model fine-tuning under the mentorship of senior researchers.

You’ll work with ML teams, QA leads, and delivery managers to curate high-quality training data for generative models, contribute to targeted model experiments, and refine workflows for controllable and high-fidelity video generation. Strong cross-functional communication is essential to transform modeling requirements into actionable data specs and evaluation criteria.

Key Responsibilities

Data Curation for Video Generation

• Co-develop structured guidelines for datasets powering generative models, with an emphasis on:
• Prompt-conditioned video generation (text-to-video, image-to-video)
• Consistency in motion, temporal coherence, and visual quality
• Control signal labeling (e.g., camera trajectories, depth, optical flow)
• Style, identity, or scene constraints across frames
• Collaborate with ML stakeholders to align data specs with generation tasks such as video inpainting, editing, expansion, or simulation-to-real synthesis.
• Analyze where dataset limitations are causing poor outputs or failure cases in generative benchmarks (e.g., Video-Bench, VBench, or internal video synthesis metrics).
• Recommend guideline updates and synthetic data augmentations based on output inspection and metric evaluations (FID, IS, CLIP similarity, etc.).
• Assist in fine-tuning or conditioning small video generation models (e.g., diffusion-based or transformer-based architectures) under the guidance of senior engineers.
• Run targeted experiments to evaluate model behavior across data variations and prompt types.

QA and Evaluation Process Support

• Build structured review protocols for evaluating generated video content, including:
• Temporal jitter or drift
• Semantic coherence with prompts
• Help define feedback loops for iterative data refinement and edge-case tracking.

Cross-Functional Communication

• Act as a liaison between ML engineers, data operations, and evaluation teams to ensure dataset alignment with generative objectives.
• Produce clear documentation and updates on data design, annotation strategy, and model performance feedback.

Qualifications

• 3–5 years of experience in computer vision, applied ML, or generative AI, especially with video, image, or temporal modeling.
• Familiarity with video synthesis techniques, including diffusion models, transformer-based generators, or GAN variants.
• 3+ years of experience writing production-quality software, preferably in machine learning, AI, or data science contexts.
• Proficiency in Python and familiarity with libraries such as PyTorch, Keras, scikit-learn, and Hugging Face.
• Hands-on experience with basic fine-tuning or evaluation of generative models (e.g., with PyTorch, TensorFlow, Hugging Face, or Runway).
• Exposure to tools or platforms for dataset curation and video inspection (e.g., CVAT, custom viewers, or synthetic data generators).
• Strong grasp of the data lifecycle in generative AI—annotation, prompt engineering, synthetic data usage, and evaluation.
• Ability to read ML/AI research papers (e.g., on video diffusion, text-to-video, or controllable generation) and apply insights to dataset or model design.
• Excellent communication skills—confident in presenting technical findings, coordinating with stakeholders, and translating between research and delivery teams.

What Success Looks Like

• Curated datasets that enhance fidelity, consistency, and controllability in generated video outputs.
• Documented guidelines and QA protocols that improve model outputs across real-world tasks and benchmarks.
• Targeted fine-tuning or conditioning efforts that lead to measurable improvements in generation quality.
• Seamless collaboration across ML, data, and QA teams, accelerating the development and deployment of video generation systems.

Seniority level

• Seniority level

  Associate

Employment type

• Employment type

  Full-time

Job function

• Job function

  Research and Information Technology

• Industries

  Technology, Information and Internet and IT Services and IT Consulting

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Inferred from the description for this job

Medical insurance

Vision insurance

401(k)

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