Machine Learning Engineer – Perception & Prediction for Autonomous Vehicles - Austin TX The Role
We are looking for a skilled Machine Learning Engineer with experience in Python, C++, and Java (candidates may prefer any of these languages) to join our team working on cutting-edge autonomous vehicle technologies. The ideal candidate will work on perception and prediction tasks, focusing on detecting objects around vehicles or robots, as well as predicting their future positions according to motion and traffic laws.
As part of the team, you'll be responsible for building and optimizing machine learning algorithms to solve complex tasks that enable autonomous vehicles to navigate and interact safely in dynamic environments.
Key Responsibilities:- Develop and implement perception algorithms to detect and track objects surrounding the vehicle/robot.
- Design and deploy prediction models that forecast the future state of objects based on motion and traffic laws.
- Contribute to the development and application of machine learning models that run before a vehicle is deployed for real-world city driving.
- Collaborate on research and development of deep learning techniques, particularly transformers, to improve model performance.
- Work with large-scale datasets and utilize advanced ML algorithms, focusing on reinforcement learning for autonomous driving applications.
- Optimize models for real-time inference, ensuring efficiency and accuracy in complex environments.
- Contribute to system design and architectural decisions related to machine learning applications in autonomous vehicles.
- Stay up to date with advancements in deep learning and machine learning to continuously enhance model capabilities.
- Proven experience in machine learning, deep learning, and reinforcement learning, particularly in the context of perception and prediction tasks for autonomous vehicles or robotics.
- Strong programming skills in Python, C++, or Java (candidate’s preference).
- Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or similar.
- Knowledge of transformers, large-scale model training, and complex machine learning algorithms.
- Practical experience in solving industry-scale machine learning problems, with a focus on perception and prediction.
- Professional experience in training large-scale machine learning models, including reinforcement learning and deep learning techniques.
- Familiarity with the challenges of deploying machine learning models in real-world environments.
- Strong problem-solving skills and the ability to work collaboratively in a team-oriented environment.
- Experience working with sensors used in autonomous vehicles (e.g., LIDAR, radar, cameras).
- A strong understanding of motion and traffic laws as they pertain to autonomous vehicle navigation.
- Experience with model optimization for real-time applications.
