Research Engineer, Robotics, DeepMind
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Minimum qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, Robotics, or equivalent practical experience.
- 3 years of experience developing, integrating, and debugging software for simulated and physical robotic systems.
- Experience with physics simulation environments (such as MuJoCo, NVIDIA Isaac Sim, or Gazebo) for modeling robots, testing software stacks, or validating sim-to-real transfer.
- Experience integrating sensor streams (cameras, IMUs, encoders, or tactile sensors), actuator interfaces, and real-time control loops into a robotics software stack.
Preferred qualifications:
- 5 years of experience building core components of a robotics software stack, SDK, or hardware abstraction layer.
- Experience developing, deploying, or running machine learning policies (such as Vision-Language-Action models or reinforcement learning controllers) on physical robots.
- Experience with system identification, actuator and sensor modeling, or closing the sim-to-real gap for manipulation or locomotion.
- Experience with teleoperation systems, motion tracking hardware, wearable data collection devices, or multi-fingered dexterous hands.
- Experience designing automated calibration verification, system health monitoring, or hardware-in-the-loop (HIL) test pipelines.
About the job
At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
As a Robotics Research Engineer, you will connect physical and simulated robot platforms to our research and machine learning software stack. Your core focus is to build a unified software interface across physics simulation and physical hardware so that models, teleoperation systems, and evaluation tools run seamlessly in both domains. Working in our Cambridge lab alongside hardware, simulation, and machine learning teams, you will bring up new robotic platforms, end effectors, and wearable devices and make them ready for research.
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.
US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Integrate robot control software and hardware interfaces with our common robotics SDK across simulated and physical platforms, end effectors, and wearable devices.
- Build, validate, and maintain physics simulation environments and software-in-the-loop harnesses that accurately model the on-robot software stack, control loops, and hardware interfaces.
- Run and maintain the on-robot execution harness to call local and remote machine learning action models in real time.
- Connect platforms to the teleoperation and data logging stack to capture synchronized sensor streams, robot actions, and demonstrations for model training.
- Develop platforms with fleet orchestration and evaluation tools to run standardized benchmarks and guide data collection.
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