The posting
Reporting to the CEO directly, you are responsible for developing and optimize cutting-edge autonomy technologies for our next generation vehicle product.
As a venture-backed startup, we are pioneering the next generation of autonomous driving with AI-Defined Vehicles - a leap beyond the latest software-defined vehicles. Our mission is to build the world's most intelligent autonomous vehicle that caters to your every need, before you even know it.
We are looking for a Sr. Machine Learning Engineer to develop and optimize cutting-edge autonomy. If you have industry experience and are passionate about pushing the boundaries of machine learning, LLM/LVMs, and autonomous systems, we want you on our team.
Role Description Develop, fine-tune, and optimize deep learning systems for autonomy, perception, and decision-making. Research and implement multi-modal AI systems, combining vision, language, and reinforcement learning. Enhance self-supervised and semi-supervised learning methods for training models on large-scale driving data. Collaborate with software, simulation, and cloud engineering teams to deploy ML models into production-grade autonomy stacks. Design and maintain scalable data pipelines for ingesting and processing sensor fusion data (LiDAR, radar, cameras). Optimize model inference for real-time performance on embedded and cloud-based platforms. Conduct model evaluations, performance tuning, and failure analysis to improve robustness and generalization.
Qualifications Industry (non-academic) experience is required, post graduation. 3+ years of experience in machine learning, deep learning, or AI engineering. Expertise in LLM/LVM model architectures, training techniques, and fine-tuning. Strong background in autonomous systems, reinforcement learning, or robotics. Hands-on experience with computer vision for perception tasks (e.g., object detection, segmentation, sensor fusion). Proficiency in Python, TensorFlow, PyTorch, and deep learning frameworks. Experience with AWS (S3, EC2, SageMaker, Lambda, etc.) for ML training and deployment. Knowledge of data engineering practices for large-scale ML pipelines. Strong algorithmic and problem-solving skills, with experience optimizing models for embedded and cloud-based environments. Experience working with autonomous driving stacks. Familiarity with distributed training, federated learning, and on-device AI optimization. Exposure to self-supervised learning, generative AI, and multi-modal architectures. Understanding of simulation environments for AI model validation. Knowledge of automotive systems and functional safety requirements is preferred.
