Machine Learning Engineer
Description For Machine Learning Engineer
Wayve, founded in 2017, is at the forefront of autonomous driving technology, developing Embodied AI solutions that are revolutionizing the automotive industry. As a Machine Learning Engineer, you'll be instrumental in creating end-to-end neural networks for autonomous driving systems. The role combines cutting-edge research with practical implementation, requiring expertise in large-scale deep learning and data engineering.
The position offers a unique opportunity to work with advanced AI foundation models and contribute to the future of autonomous driving. You'll collaborate with both Production and Research teams, focusing on developing, testing, and deploying sophisticated ML models. The company values technical excellence, innovation, and a commitment to quality in data engineering.
Wayve offers a dynamic, fast-paced environment where learning and growth are paramount. The company maintains a hybrid working model, combining office time for innovation and collaboration with flexible remote work options. Their culture emphasizes inclusivity, continuous learning, and the chance to make a significant impact in a relatively small but ambitious organization.
This role is perfect for someone who combines strong technical skills with a passion for autonomous driving technology. You'll be part of a mission-driven organization that's pushing the boundaries of what's possible in autonomous vehicles, working with some of the brightest minds in the field while enjoying competitive benefits and a supportive, ego-free culture.
Responsibilities For Machine Learning Engineer
- Drive the development and productionization of end-to-end neural networks for autonomous driving
- Collaborate with Production and Research teams
- Develop, test, scale, and deploy cutting-edge ML models
- Work on data/model parallelism techniques, model optimization and quantization
Requirements For Machine Learning Engineer
- 3+ years experience shipping AI and ML features in production environments
- Strong programming skills in Python, including experience with deep learning frameworks such as PyTorch, Numpy, Pandas
- Solid understanding of training, validation, and testing methodologies for deep learning models
- Experience in designing efficient large-scale machine learning systems
- Commitment to excellence in data engineering
- MS or PhD in Computer Science or Engineering (preferred)
- Record of publication at places like CoLM, CoRL, CVPR, ICCV, AAAI, ICML, NeurIPS, RSS, ICRA, ACL, EMNLP (preferred)
Benefits For Machine Learning Engineer
- Competitive compensation and benefits
- Dynamic and fast-paced work environment
- Learning opportunities from industry experts
- Formal learning support
- Flexible core working hours
- Hybrid working policy
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