Currently pursuing a Bachelor's degree in software engineering or a related field
Working knowledge of Python
Understanding of ML fundamentals for training and evaluating models
Ability to use agents for learning and implementation effectively
Familiarity with distributed systems analysis or optimization (preferred)
Knowledge of model performance techniques like sharding, quantization, and batching (preferred)
Working knowledge of C++ (preferred)
Responsibilities
Read and write pipeline and implementation code
Profile and analyze slowness within distributed systems
Build custom harnesses to isolate system components
Query existing logs and data sources for aggregate statistics
Optimize evaluation pipeline at orchestration and model levels
Provide insights on pipeline runtime and success rates
Integrate agents with evaluation workflows effectively
Benefits
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
Free breakfast, lunch, dinner, and snacks
Free access to Google shuttles
Onsite gym
Full Job Description
The subteam for this role will be:
The Model Eval team has a lot of projects that evolve quickly. We will help the Intern find a project that matches their skill set during the internship. Possible projects include:
Eval pipeline optimization (orchestration level): Finding opportunities to decrease e2e execution cost by reducing overhead or increasing parallelism in jobs
Eval pipeline optimization (model level): Improving eval workload efficiency by improving exported model efficiency in CPU or TPU export configurations
Pipeline observability: Providing insights and high level metrics on pipeline runtime and success rate
LLM integration: Finding better ways to integrate agents with eval workflows. This could be making it easier for agents to run evals accountably, or making it easier for agents to contribute maintainable code to the eval codebase
You will:
Reading/writing pipeline and implementation code
Profiling and understanding slowness within distributed systems
Building custom harnesses to isolate components of the system
Querying existing logs and data sources for aggregate statistics
You have:
Currently pursuing a Bachelor's degree in software engineering or a related field
Working knowledge of Python
Understanding of ML fundamentals of training and evaluating models
The ability to use agents for learning and implementation, and the wisdom to know when each is appropriate
We prefer:
Distributed systems analysis or optimization
Model performance techniques like sharding, quantization, and batching
Working knowledge of C++
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 Bachelors Pay
$60-$60 USD
About Waymo
Waymo LLC is a self-driving technology development company. It is a subsidiary of Alphabet Inc., the parent company of Google. Waymo develops autonomous driving technology and provides ride-hailing services through its Waymo One program. The company has been testing its self-driving technology on public roads since 2009 and has logged millions of miles of autonomous driving. Waymo has partnerships with several automakers and has been working on developing autonomous trucks for use in logistics.
