# Machine Learning Engineer, Motion Planning & Prediction

**Company:** [Avride](https://hotfix.jobs/companies/avride)
**Location:** Austin, TX
**Role:** ML Engineering
**Skills:** Python, PyTorch, TensorFlow, JAX, C++, Transformers, MLOps, MLflow, Kubeflow, Weights & Biases, Spark, Ray
**Posted:** 2026-04-10

> Develops and deploys deep learning models for motion planning and behavioral prediction in autonomous vehicles using petabytes of driving data. Requires strong Python, PyTorch/TensorFlow expertise, full ML lifecycle experience, and C++ for real-time inference.

## Job Description

## What You'll Do

- Design, train, and deploy state-of-the-art machine learning models for behavioral prediction and motion planning
- Develop robust data pipelines to process, clean, and label massive-scale vehicle sensor and simulation datasets
- Work with deep learning architectures such as transformers to model complex temporal interactions between traffic agents
- Establish and own the metrics for model performance, and create evaluation frameworks that correlate with on-road safety and performance
- Collaborate with software engineers to integrate and optimize trained models for real-time inference on the vehicles embedded hardware
- Stay current with the latest research in machine learning, imitation learning, and reinforcement learning, and apply novel techniques to our systems

## What You'll Need

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

## 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 problems in robotics, 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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