Avride is seeking a Machine Learning Engineer to join its autonomous vehicle team focused on motion planning and prediction in Austin, TX (onsite).

Design, train, and deploy advanced ML models for behavioral prediction and motion planning.
Build robust data pipelines to process, clean, and label large-scale vehicle sensor and simulation datasets.
Leverage transformers and other deep learning architectures to capture temporal interactions among traffic agents.
Define and own model performance metrics; develop evaluation frameworks aligned with on-road safety and performance.
Collaborate with software engineers to integrate and optimize models for real-time on-vehicle embedded hardware inference.
Stay current with ML research, including imitation learning and reinforcement learning, and apply novel techniques to the systems.

Strong Python skills and hands-on experience with modern deep learning frameworks such as PyTorch, TensorFlow, or JAX.
Solid grounding in ML fundamentals, including neural network architectures, training methodologies, and evaluation techniques.
Experience across the ML lifecycle, from data exploration and prototyping to deployment and monitoring.
Proficiency in C++ for high-performance model inference code.

Strong track record in ML competitions such as Kaggle or notable contributions to major open-source ML projects.
Experience applying ML to robotics problems like behavioral prediction, motion planning, or computer vision.
Experience with MLOps tools and platforms such as MLflow, Kubeflow, Weights & Biases.
Experience with large-scale distributed data processing and training frameworks such as Spark or Ray.
Publications in top ML or robotics conferences (e.g., NeurIPS, ICML, CVPR, ICLR, CoRL, RSS).

Machine Learning Engineer, Motion Planning & Prediction

Job Description