Turing
Applied Research Engineer - Video Data & ML
Turing, San Francisco, California, United States, 94199
Join to apply for the
Applied Research Engineer - Video Data & ML
role at
Turing Join to apply for the
Applied Research Engineer - Video Data & ML
role at
Turing Get AI-powered advice on this job and more exclusive features. 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 Direct message the job poster from Turing 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 Referrals increase your chances of interviewing at Turing by 2x Inferred from the description for this job
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Applied Research Engineer - Video Data & ML
role at
Turing Join to apply for the
Applied Research Engineer - Video Data & ML
role at
Turing Get AI-powered advice on this job and more exclusive features. 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 Direct message the job poster from Turing 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 Referrals increase your chances of interviewing at Turing by 2x Inferred from the description for this job
Medical insurance Vision insurance 401(k) Get notified about new Research Engineer jobs in
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#J-18808-Ljbffr