Machine Learning Engineer II developing production - grade ML models for fraud detection at GEICO. Collaborating on system architecture and ensuring optimal performance of fraud assessment systems.
About the role
- Senior Machine Learning Engineer developing advanced solutions for autonomous driving at Woven by Toyota. Engaging in groundbreaking challenges in machine learning and AI-driven technologies.
Responsibilities
- Own design and development of ML models or model components for end-to-end autonomous driving: ranging from initial data strategy, design, development, experimentation, evaluation and deployment.
- Resolve ambiguities and address uncertainties arising from complex projects involving multiple teams and legacy codebases.
- Enable and help other colleagues on the team to be more effective through leading by example when it comes to writing high-quality code, being rigorous with Machine Learning experimentation and knowledge sharing.
- Collaborate closely with stakeholders from multiple teams in different time-zones to define interfaces and requirements for an end-to-end stack.
Requirements
- MS, or higher degree, in related field, or equivalent industry experience
- Professional experience with ML frameworks such as PyTorch, Jax or Tensorflow (PyTorch preferred)
- Knowledge of debugging and profiling deep neural networks on NVIDIA CUDA stack
- Experience in state of the art architectures for object detection and 3D perception
- Experience in ML workflows: data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, inference optimization
- Python and C++ experience
- Hands-on experience with building a perception stack for autonomous systems
- Familiarity with recent breakthroughs in ML (e.g. foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures)
- Hands-on experience with large scale distributed training
Benefits
- Excellent health, wellness, dental and vision coverage
- A rewarding 401k program
- Flexible vacation policy
- Family planning and care benefits
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