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Open nowFirst seen 8 hours ago

Staff Research Engineer, Expressive Motion and Believability, DeepMind

Google DeepMind157 open roles

Where
San Francisco, CA, USA; Mountain View, CA, USA
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Your applicationOpen nowStaff Research Engineer, Expressive Motion and Believability, DeepMindGoogle DeepMind · San Francisco, CA, USA; Mountain View, CA, USA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Google DeepMind's own form.

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The clock on this job

Early applications get read.

13.5% of postings close within 7 days. Measured by our own scanner across the market. Google DeepMind postings stay open a median of 26 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.6%3 days
  3. 13.5%7 days
  4. 21.8%14 days
  5. 36.6%30 days
  6. 58.9%60 days
This job: first seen 8 hours ago

Google DeepMind median: 26 days open

The posting

Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: San Francisco, CA, USA; Mountain View, CA, USA.

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 8 years of experience in software development.
  • 5 years of experience testing, and launching software products.
  • 3 years of experience with software design and architecture.
  • 2 years of experience in reinforcement learning, imitation learning, multimodal generative modeling, training and inference, and large vision, vision-language, video, and other multimodal models.
  • 1 year of experience building generative architectures or world models applied to video, robotics, or interactive simulation environments.

Preferred qualifications:

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • 8 years of experience with data structures and algorithms.
  • 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  • 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.

About the job

As a Research Engineer on the Companion Robots team, you will train expressive, emotionally legible motion—including gait, gaze, posture, and gesture that change visibly with the robot's internal state—and develop the metrics to evaluate its effectiveness. You will work directly with animators and character designers, utilizing their expertise as a tuning pass and training signal, while building the evaluation harness required to compare behaviors, models, and robot platforms. Furthermore, you will collaborate with our state-of-the-art video generation and interaction models, such as Omni or Code Genie, to create systems capable of generating real-time expressive behavior in response to stimuli and internal motivations.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • Work with omni/omni-live to extract animation, fine tune models to generate real time behavior and expression in response to stimulus and internal motivation of the character.
  • Design, train and evaluate reinforcement learning and imitation learning algorithms for lifelike movement, gaze and posture, conditioned on the robot's affective and motivational state.
  • Work with animators to convert a library of hand-authored reference motion into style-conditioned policies, using adversarial motion priors and related techniques, so that character can be authored rather than hand-tuned.
  • Create scenario suites, batch simulation, video capture, and automated critics that assess behavior reads correct to an observer.
  • Run paired human comparisons with designers and human-factors partners, and report how well automated critics correlate with human judgment.
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