Staff Machine Learning Engineer (Vision-Language Foundation Models)

Join the Oracle Perception team as a Staff Machine Learning Engineer, where you will lead the technical strategy for multimodal pre-training datasets, design and implement state-of-the-art fine-tuning and reinforcement learning pipelines, and architect scalable inference and evaluation pipelines. You will also define training recipes and scaling laws, drive cross-functional AI strategy, and provide staff-level technical leadership. This is an opportunity to redefine the foundation of autonomous driving and make a significant impact in the field of AI and machine learning.

• Conduire la stratégie technique pour la création de jeux de données de pré-entraînement multimodaux à grande échelle.
• Concevoir et mettre en œuvre des pipelines de fine-tuning et d'apprentissage par renforcement de pointe.
• Architecturer des pipelines d'inférence et d'évaluation hautement évolutifs qui exploitent ces modèles.
• Proven expertise in the full lifecycle of Foundation Models: from pre-training data curation (interleaved formats, tokenization) and distributed training to advanced post-training techniques
• 8+ years of hands-on experience designing, training, and scaling deep learning models, with at least 3+ years focused deeply on training Large Language Models (LLMs) or Vision-Language Models (VLMs)
• Expert-level understanding of training infrastructure and distributed paradigms (e.g., FSDP, Megatron, JAX/Pax) required for training massive models reliably
• Expert-level software engineering fundamentals using Python, PyTorch, or JAX, with a track record of building reliable, highly scalable ML systems
• Proven ability to operate with high ambiguity, define technical roadmaps, and drive complex, multi-quarter technical initiatives across multiple teams in a fast-paced environment
• Master’s degree in Computer Science, AI, ML, or a related technical field
• PhD in Computer Science, Artificial Intelligence, or a related field
• Strong publication record in top-tier AI venues (e.g., NeurIPS, ICML, ICLR, CVPR) focusing on foundation models, large-scale training, reinforcement learning, or reasoning
• Demonstrated experience in Data Engineering for Foundation Models at the scale of billions/trillions of tokens (e.g., deduplication, quality filtering, synthetic data generation)
• Deep experience with advanced Reinforcement Learning paradigms applied to language or vision tasks (focusing on improving System 2 thinking, logical deduction, and model alignment)
• Familiarity with the systemic challenges of multimodal perception in robotics or autonomous driving (e.g., 3D scene understanding, trajectory prediction)
• A proven track record of Staff-level impact: influencing product direction, pioneering zero-to-one ML architectures, and multiplying team efficiency through technical leadership