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greenhouse

2027 Summer Intern, BS, Software Engineer, Model Eval

Waymo
USA
Internship, Full Time
Entry
Hybrid
USD 60-60 hourly / hour
Discovered 2 weeks ago

Full Job Posting

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

Requirements

  • 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

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