Machine Learning Engineer (Robotics)

TL;DR

Machine Learning Engineer (Robotics): Developing and training deep learning models for autonomous vehicle motion planning and behavioral prediction with an accent on large-scale data processing and real-time system performance. Focus on building robust behavioral prediction systems, optimizing inference on embedded hardware, and ensuring safety through rigorous model evaluation.

Location: Must be based in Austin, TX; Onsite position.

Company

hirify.global builds core software and data processing systems for autonomous vehicle motion planning and decision-making.

What you will do

• Design, train, and deploy state-of-the-art ML models for behavioral prediction and motion planning.
• Develop robust data pipelines to process, clean, and label massive-scale sensor and simulation datasets.
• Utilize transformer architectures to model complex temporal interactions between traffic agents.
• Create performance evaluation frameworks that correlate with on-road safety metrics.
• Collaborate with software engineers to optimize models for real-time inference on vehicle embedded hardware.
• Apply research in imitation and reinforcement learning to production systems.

Requirements

• Must be authorized to work in the U.S.
• Strong proficiency in Python and modern deep learning frameworks like PyTorch, TensorFlow, or JAX.
• Solid understanding of neural network architectures, training methodologies, and ML fundamentals.
• Experience with the full machine learning lifecycle from prototyping to deployment.
• Proficiency in C++ for performance-critical model inference.

Nice to have

• Track record in ML competitions or open-source contributions.
• Experience applying ML to robotics problems such as motion planning or computer vision.
• Familiarity with MLOps tools like MLflow, Kubeflow, or Weights & Biases.
• Experience with distributed data processing frameworks like Spark or Ray.
• Publications in top-tier conferences such as NeurIPS, CVPR, or CoRL.