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28d ago

Machine Learning Engineer, Motion Planning & Prediction

Avride Austin, TX

Austin, TX

28d ago

Full time Ai Ml Artificial Intelligence Big Data

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Job Description

Avride is seeking a Machine Learning Engineer to join its autonomous vehicle team in Austin, TX on site, focusing on motion planning and prediction. The role entails building end-to-end ML models, developing data pipelines, and enabling real-time inference on embedded hardware, while applying cutting-edge ML research.

Responsibilities

Develop and operationalize advanced machine learning models for behavioral prediction and motion planning
Build scalable data pipelines to process, clean, and label large-scale vehicle sensor and simulation datasets
Leverage architectures such as transformers to capture temporal interactions among traffic agents
Define and own model performance metrics and create evaluation frameworks aligned with on-road safety and performance
Collaborate with software engineers to integrate models and optimize real-time inference on embedded vehicle hardware
Maintain awareness of the latest ML research, including imitation learning and reinforcement learning, and apply novel techniques to systems

Requirements

Proficient in Python with hands-on experience in modern deep learning frameworks (PyTorch, TensorFlow, or JAX)
Solid understanding of ML fundamentals, including neural network architectures, training methodologies, and evaluation techniques
Experience across the full ML lifecycle, from data exploration and prototyping to deployment and monitoring
C++ proficiency for writing high-performance model inference code

Technologies

Python
PyTorch
TensorFlow
JAX
C++
MLflow
Kubeflow
Weights & Biases
Spark
Ray

Nice to have

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

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