Master Thesis focusing on developing machine learning models for lithium - ion cell sorting at Fraunhofer LBF. Involvement in innovative projects addressing circular economy in battery recycling.
About the role
- Machine Learning Engineer developing Foundation Models for ML Agents at Zoox. Collaborating with teams to validate driving performance and generate human-like driving behavior.
Responsibilities
- You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like agents.
- You will work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism.
- You will contribute to our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field
- You will develop metrics and tools to analyze errors and understand improvements of our systems
- You will collaborate with engineers on Perception, Planning, Simulation, and Validation to solve the overall Autonomous Driving problem.
Requirements
- PhD degree in computer science or related field +1y of professional experience (top tier publications can remove the need for the year of experience) or, MSc +5y of professional experience in a relevant field.
- Experience in Planning and / or Prediction using Reinforcement Learning techniques
- Experience with training and deploying transformer-based model architectures
- Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
- Fluency in Python with a basic understanding of C++
Benefits
- paid time off (e.g. sick leave, vacation, bereavement)
- unpaid time off
- Zoox Stock Appreciation Rights
- Amazon RSUs
- health insurance
- long-term care insurance
- long-term and short-term disability insurance
- life insurance
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