MS or PhD in Machine Learning, Computer Vision, Robotics or related quantitative fields, or equivalent industry experience.

3+ years of experience with Python, any major deep learning framework (PyTorch preferred), and software engineering best practices

3+  years of experience with deep learning approaches such as supervised/unsupervised learning, transfer learning, multi-task learning, and/or deep reinforcement learning.

3+ years of experience covering machine learning workflows, data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, and inference optimization.

Experience working with large-scale foundation models, including pretraining, multimodal architectures, self-supervised learning approaches.

Deep understanding of runtime complexity, distributed/cloud ML infrastructure, data pipeline architecture,resource-aware optimization.

Comfortable in writing C++ code to help integrate with our autonomous vehicle platform.

Strong leadership skills to influence others and the team's technical strategy.

Strong communication skills with the ability to communicate concepts clearly and precisely.

Published research at top-tier conferences (NeurIPs, CVPR and similar).

Proven track record of deploying ML models at scale in self-driving or related fields.

Experience with offboard, auto-labeling, or "data-engine" pipelines, including mining, curation, active learning, and ground-truth-free evaluation for large-scale datasets.

Hands-on experience with vision-language models (VLMs), world models, video prediction, or latent dynamics models for autonomous systems or robotics.

Experience leveraging foundation models across multiple platforms and sensor setups, including distilling larger models into efficient real-time variants.

Familiarity with production-level coding and deployment onto embedded platforms, optimizing for latency and hardware constraints.

Experience in self-driving challenges (Perception, Prediction, Mapping, Localization, Planning, Simulation).