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# Research Engineer, Human Understanding, DeepMind

- **Company:** Google LLC
- **Location:** Los Angeles, CA, United States
- **Experience:** Expert
- **Salary:** $174,000.0 - $253,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Machine Learning, Language Modeling, Systems Integration, Reinforcement Learning, Deep Learning, Generative AI, Information Technology
- **Published:** July 11, 2026
- **Apply:** https://dejobs.org/x/x/1AA3DF221F214BCC814B94FFB37C012B/job/

## About the Role

* Bachelor's degree in Computer Science, Machine Learning, a related technical field, or equivalent practical experience.
 * 5 years of experience developing machine learning models (e.g., audio/speech-visual models).
 * Experience working with and tuning vision language models.
 * Experience programming in Python and with deep learning frameworks (e.g., JAX, Flax, or Gemax).
 * Experience conducting research and development, including experimental design, implementation, and analysis., * Experience with Generative AI techniques and architectures.
 * Experience with multimodal learning, integrating information from different data types (e.g., vision, audio, text).
 * Familiarity with Reinforcement Learning or alignment methods.
 * Understanding of privacy-preserving machine learning or responsible AI practices.
 * A track record of publications in AI/ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV).

## Description

* Research and implement novel models and other multimodal techniques for a more holistic understanding of human likeness across visual, audio, and textual data.
 * Conduct experimental research cycles from hypothesis to deployment, focusing on areas like scalable deepfake detection, privacy-preserving matching, and consistent human likeness generation.
 * Take ownership of substantial technical projects within human likeness modeling (HLM), from ideation and design to implementation and evaluation, often involving cross-functional collaboration.
 * Inform and contribute to the development of scalable and efficient research infrastructure for HLM models and datasets.
 * Design and execute strategies for tuning and adapting vision-language models (VLMs) and other foundation models for specific HLM tasks, such as improved explainability and nuanced likeness measurement.


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