Machine Learning Engineer

About the RoleOur client is looking for a creative and driven Machine Learning Engineer to join our autonomous vehicle team, with a primary focus on advancing motion planning systems. In this role, you will help build the intelligence that enables vehicles to make safe, real-time decisions in complex, dynamic environments. You’ll work at the intersection of machine learning, robotics, and control systems - developing models that not only understand the world, but actively determine how the vehicle should move through it. If you’re excited about solving high-stakes planning challenges and shaping the decision-making layer of autonomous systems, we’d love to connect.What You’ll DoDesign, train, and deploy state-of-the-art machine learning models specifically for motion planning and decision-making in real-world driving scenariosDevelop models that generate safe, efficient, and human-like trajectories by reasoning over dynamic agents, uncertainty, and long-term outcomesBuild and maintain scalable data pipelines to process and learn from large-scale sensor and simulation datasets, with a focus on planning-relevant featuresApply advanced deep learning architectures (e.g., transformers, sequence models) to capture temporal dependencies and predict multi-agent interactions that inform planning decisionsDefine and own planning-centric evaluation metrics, ensuring model performance aligns with safety, comfort, and real-world driving behaviorCollaborate closely with software and systems engineers to integrate planning models into real-time, on-vehicle inference systems with strict latency constraintsExplore and apply techniques from reinforcement learning, imitation learning, and optimization to improve planning robustness and adaptabilityStay at the forefront of research in motion planning, decision-making under uncertainty, and autonomous systemsWhat You’ll Need:Strong proficiency in Python and hands-on experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX)Deep understanding of machine learning fundamentals, with particular interest in sequential modeling, decision-making, or control systemsExperience across the full ML lifecycle, with exposure to deploying models in real-time or safety-critical environmentsProficiency in C++ for implementing high-performance inference and planning systemsMust be willing to work onsite 5 days a week in Austin, TX - local candidates preferred. Nice to Have:Experience applying machine learning to motion planning, behavioral prediction, or robotics decision-making problemsFamiliarity with reinforcement learning, trajectory optimization, or control theoryExperience with MLOps tools (e.g., MLflow, Kubeflow, Weights & Biases) and scalable training frameworks (e.g., Spark, Ray)Contributions to open-source ML projects or strong performance in competitions (e.g., Kaggle)Publications in top-tier ML or robotics conferences (e.g., NeurIPS, ICML, ICLR, CoRL, RSS, CVPR)