You will:- Implement uncertainty aware algorithms that improve the performance of transformer-based multi-task classifiers
- Implement the changes end-to-end, including eval
- Ensure both data and model implementation are correct according to the experiment plan. Pay attention to details about the mechanics of the modeling setup
You have:- Currently enrolled in a PhD program in Computer Science, Robotics, Electrical Engineering, or a related quantitative field
- Strong programming proficiency in Python and hands-on experience with deep learning frameworks (e.g., TensorFlow, JAX)
- Solid theoretical understanding of machine learning and deep learning fundamentals, including debugging transformer based models with TensorBoard metrics
- Familiarity with software development best practices, including version control
We prefer:- Uncertainty measurement in deep learning model development
- Hands-on experience using data to improve model performance, as opposed to only focusing on architectural model improvements
General Perks- Help solve challenging problems with a direct impact on the company
- Competitive compensation packages with a housing/relocation bonus (if applicable)
- Medical, dental, and vision insurance
- Fun intern events and networking opportunities
Onsite Perks- Free breakfast, lunch, dinner, and snacks
- Free access to Google shuttles
- Onsite gym
Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in.
The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company's generous benefits programs, subject to eligibility requirements.
Hourly PhD Pay
$85-$85 USD
