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1mo ago

Machine Learning Engineer, Motion Planning & Prediction

Avride Austin, TX

Austin, TX

1mo ago Closed

Full time Ai Ml Artificial Intelligence Big Data

This position is no longer accepting applications (closed Jul 16, 2026).

Job Description

Avride seeks a Machine Learning Engineer to join its autonomous vehicle team in Austin, TX (onsite), focusing on motion planning and prediction, building end-to-end ML models, data pipelines, and real-time inference on embedded hardware.

Responsibilities

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 data
Leverage transformer-based architectures to model complex temporal interactions among traffic agents
Define and own model performance metrics, and develop evaluation frameworks aligned with on-road safety and effectiveness
Collaborate with software engineers to integrate and optimize models for real-time onboard inference on embedded hardware
Stay current with advances in ML, imitation learning, and reinforcement learning, applying novel techniques to our systems

Requirements

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

Technologies

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

Nice to Have

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

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