Research Scientist, Model Optimization

Snapshot

Artificial Intelligence could be one of humanity’s most useful inventions. We’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

About Us

We are looking for Research Scientists to join the Robotics team whose mission is to build “Embodied AI” to power the next generation of helpful robots.

Job Summary:

We are looking for a Research Scientist to join our robotics team and push the boundaries of on-device model optimization. You will work on developing novel end-to-end models, improving their accuracy, efficiency, and robustness for deployment on physical devices like GPUs and robots.

Responsibilities:

• Design, train, and evaluate deep learning models for various on-device applications.
• Develop novel algorithms for improving model performance and efficiency, including but not limited to:
• End-to-end model architectures.
• Optimization techniques for inference on constrained hardware.
• Techniques for domain adaptation and semi-supervised learning.
• Confidence estimation and uncertainty modeling.
• Work on related machine learning tasks such as model compression, quantization, and efficient resource utilization.
• Optimize models for on-device and streaming applications, specifically considering latency, computational constraints, and deployment on physical devices such as GPUs, embedded systems, and robots.
• Collaborate with other researchers and engineers to integrate your work into products.

Minimum Qualifications:

• PhD in Computer Science, Electrical Engineering, or a related field with a focus on machine learning, deep learning, or embedded systems, or equivalent practical experience.
• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Strong programming skills in Python or C++.
• Experience with large-scale data and distributed training.
• Experience with on-device machine learning and model optimization for various hardware platforms, including GPUs, TPUs, and embedded systems.
• Experience with real-time systems and deployment on physical devices.

Preferred Qualifications:

• A strong publication record in top-tier machine learning or systems conferences (e.g., NeurIPS, ICML, MLSys, CVPR, ICCV).
• A passion for solving challenging research problems and a desire to make a real-world impact.

The US base salary range for this full-time position is between $248,000 - $349,000 + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.

We value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.