We are seeking a hands-on Applied AI Scientist to join our core R&D team and drive the development of next-generation AI systems for autonomous driving. This role sits at the intersection of applied research and deployment. This role sits at the intersection of applied research and deployment. You will work directly on our multi-layered autonomy architecture, with a primary focus on real-time predictive models for driving decisions. A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.

Responsibilities:

• Own the research-to-deployment cycle for driving models - from literature review and prototyping through to production integration.
• Design, implement, and iterate on real-time predictive models, including vision-language-action (VLA) models.
• Collaborate on reasoning systems, contributing to VLA models that handle planning across varied horizons.
• Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms.
• Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack.
• Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar.
  
  

Requirements:

• Ph.D. in Computer Science, Electrical Engineering, Machine Learning, Robotics, or a related field (an MSc with an exceptional background will also be considered).
• Strong publication or deployment track record in one or more of: deep learning, computer vision, generative AI, reinforcement learning, or motion prediction.
• Demonstrated ability to go from paper to working implementation - not just theory, but shipped systems.
• Strong coding skills in Python; experience with C++ is a plus.
• Familiarity with modern ML infrastructure: PyTorch, ONNX, Triton, Dynamo, distributed training, model optimization.
• Solid mathematical foundations in probability, optimization, and statistics.
  
  

Attributes:

• Experience with CUDA or low-level GPU optimization.
• Hands-on work with model quantization, distillation, or efficient inference on edge devices.
• Background in real-time, safety-critical, or embodied AI systems (robotics, autonomous vehicles, drones, etc.).
• Experience with foundation models (Language, Vision, Tabular, VLAs) and their on-device deployment.
• Familiarity with driving datasets, simulation environments, or sensor fusion pipelines.