Applied AI Engineer - Multimodal Transformers

Job Summary :

hackajob is collaborating with Kodiak to connect exceptional tech professionals for the role of Applied AI Engineer - Multimodal Transformers. In this role, you will design and develop multimodal transformer architectures that fuse various sensor data, research attention mechanisms, and build scalable training pipelines for large-scale models.

Responsibilities : • Design and develop multimodal transformer architectures that fuse camera, LiDAR, and radar into unified representations • Research and implement cross-modal attention mechanisms, token fusion strategies, and efficient multi-stream tokenization • Build scalable training pipelines for large-scale multimodal transformers across massive real-world datasets • Explore self-supervised and contrastive pretraining objectives that learn transferable multimodal representations • Optimize transformer models for real-time inference under latency and compute constraints

Qualifications : Required : • BS, MS, or PhD in AI, Computer Science, or a related field, or at least 2-3 years of industry experience • Experience with transformer architectures, particularly in multimodal or multi-stream settings • Familiarity with cross-attention, token fusion, or modality alignment techniques • Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow • Strong understanding of scalable training for large models, including distributed training and mixed-precision optimization • Passion for building AI that reasons over the full breadth of sensory input to operate safely in the real world

Company :

The AI-native tech hiring platform trusted by enterprises, scale-ups, and 1M+ tech professionals worldwide. Founded in 2014, the company is headquartered in London, GBR, with a team of 51-200 employees. The company is currently Growth Stage.