Plus

Senior/Staff Machine Learning Engineer, Planning

Plus

full-time

Posted on:

Location Type: Hybrid

Location: Santa ClaraCaliforniaUnited States

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Salary

💰 $130,000 - $220,000 per year

Job Level

About the role

  • Use complex map and perception sensor output to develop novel deep learning models for our planning stack.
  • Design critical safety checks to ensure driving trajectories are feasible and follow rules of road.
  • Ensure all model development keeps a real-time focus and operates efficiently in compute-constrained environments.
  • Track and incorporate the latest multidisciplinary research advancements in a fast-moving field.
  • Ensure that your work is performed in accordance with the company’s Quality Management System (QMS) requirements and contribute to continuous improvement efforts.
  • Ensure that technical work meets customer requirements, regulatory standards, and company quality policies.

Requirements

  • BS, MS, or PhD in Software, Robotics, or related field
  • 4+ years of machine learning engineering experience for robotics applications
  • Experience developing high-quality software from design and implementation to testing and deployment.
  • Expertise with Python, willingness to do some C++ development as needed.
  • Experience training with modern frameworks (e.g. PyTorch)
  • A rigorous approach to model development: running well-designed experiments, defining suitable training and validation datasets, and evaluating on the right metrics.
  • Hands-on familiarity with cloud data ingestion pipelines
  • Strong communication and collaborative skills
Benefits
  • Catered free lunch
  • Unlimited snacks and beverages.
  • Highly competitive salary and benefits package, including 401(k) plan.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
deep learningmachine learningPythonC++software developmentmodel developmentdata ingestion pipelinestestingdeploymentvalidation datasets
Soft skills
communicationcollaborationrigorous approachcontinuous improvementcustomer requirements focus