Machine Learning Engineer at HackerRank working on integrity systems to improve model quality. Collaborating on strategies for new signals like audio analysis and behavioral anomalies.
About the role
- Perception and SLAM Machine Learning Engineer developing advanced perception algorithms for autonomous machinery at AIM. Collaborating on state-of-the-art machine learning methods in challenging environments.
Responsibilities
- Advance AIM’s perception stack and sensor fusion algorithms
- This involves breaking new ground as earth moving machines don’t merely navigate on existing mapped roads – they modify and build the environment as they work which poses interesting novel challenges
- Research and development of state-of-the-art machine learning methods
- Comfortable with hands-on applications on machines deployed in the field
Requirements
- Proven track record of deployed ML-based perception systems, ideally on autonomous vehicles
- Excellent grasp of machine perception methods in 2D and 3D (depth sensing)
- Real-world experience with localization and SLAM state-of-the-art methods
- Experience shipping production models
- Solid mathematical foundation of machine learning
- Excellent software development skills in python
- Proficiency in Tensorflow or PyTorch
- Creativity and curiosity for solving complex problems
- Ideal candidate will have additional experience in several of the following areas
- Reinforcement learning
- Robotics
- Radar based perception
- Point cloud processing
- Perception sensor calibration techniques (camera, lidar, radar, etc.)
Benefits
- Receive company funded medical, dental, vision, 401k, life insurance, gourmet food & perks
- Experience a strong onsite collaboration (AIM offices, labs and proving grounds on the east side of the Greater Seattle area)
- Have an opportunity to travel to unique sites around the world (Americas, Australia, Africa & more)
Job title
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Machine Learning Engineer developing integrity systems for assessing model quality at HackerRank. Collaborating on multimodal signal processing and improving model performance.
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