ML Engineer
@ Waymo

Hybrid
$280,000
Hybrid
Full Time
Posted 4 days ago

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XXXXXXXX XXXXXXXXXXXXX XXXXXX***** @waymo.com
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Job Details

About Waymo

Waymo is a pioneering autonomous driving technology company, originally started as the Google Self-Driving Car Project. They transform mobility with safety and innovation by deploying the Waymo Driver, aimed at reducing traffic fatalities and increasing transportation access.

About the Role

The ML Engineer will join the Predictive Planning team (PrePlan) to develop and deploy advanced machine learning solutions that predict environmental states and inform the Waymo Driver's behavior. Working in a hybrid role, you will collaborate with data scientists, engineers, and stakeholders to deploy, monitor, and maintain machine learning models in production environments.

Responsibilities

  • Collaborate with cross-functional teams to deploy ML models.
  • Develop and maintain scalable CI/CD pipelines.
  • Implement best practices for model versioning, monitoring, and governance.
  • Troubleshoot deployment, performance, and scalability issues.
  • Stay up-to-date with emerging ML Ops and DevOps technologies.

Qualifications

Candidates should have a Bachelor's degree in Computer Science or related fields, with 7+ years of experience in building high-scale distributed systems or ML inference systems. Proficiency in Python and C++ is required along with hands-on experience in deploying ML models using cloud services such as GCP, AWS, and container orchestration tools like Kubernetes.

Benefits

  • Competitive salary and bonus program.
  • Equity incentive plan and comprehensive benefits.
  • Generous paid time off and flexible working arrangements.
  • Opportunities for professional development and career growth.

Key skills/competency

  • Machine Learning
  • CI/CD
  • Model Deployment
  • Python
  • C++
  • Cloud Services
  • Kubernetes
  • MLOps
  • DevOps
  • Autonomous Vehicles
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How to Get Hired at Waymo

🎯 Tips for Getting Hired

  • Customize your resume: Tailor skills to ML Ops and CI/CD.
  • Highlight cloud experience: Emphasize GCP, AWS, Kubernetes skills.
  • Showcase coding expertise: Detail Python and C++ projects.
  • Prepare for ML scenarios: Review ML frameworks and deployments.

📝 Interview Preparation Advice

Technical Preparation

Review Python and C++ coding exercises.
Practice building CI/CD pipelines in cloud environments.
Study ML frameworks like TensorFlow and PyTorch.
Brush up on Kubernetes and container orchestration.

Behavioral Questions

Describe a challenging project collaboration.
Explain time management under tight deadlines.
Share a conflict resolution experience.
Discuss handling unexpected project issues.

Frequently Asked Questions