Role Overview
We are seeking a Senior AI Research Engineer with expertise in developing and optimizing multimodal AI models. The role will be central to advancing our platform’s capabilities in inference for Generative AI, working on state-of-the-art models that integrate multiple data modalities (e.g., text, vision, and audio) for a broad range of applications.
This is an exciting opportunity to work at the intersection of advanced machine learning, in-memory computing, and high-performance AI inference on cutting-edge hardware architectures.
Responsibilities
- Model Development: Design, develop, and optimize multimodal AI models for real-time, high-efficiency inference across a variety of deployment environments (edge, server-side, and embodied AI).
- Collaboration: Work closely with cross-functional teams, including AI researchers, hardware engineers, and software engineers to integrate AI models into the broader platform.
- Scalability and Optimization: Focus on optimizing models for memory efficiency, low-latency inference, and high throughput.
- Innovation: Stay up-to-date with the latest research in multimodal AI, proposing and implementing new techniques to push the boundaries of what's possible in generative AI.
- Deployment & Testing: Implement best practices for model testing, deployment, and continuous improvement to ensure models scale effectively in production environments.
Requirements
- Experience: Proven experience (for all levels) in developing and deploying multimodal models, including text, image, and/or audio data.
- Technical Skills:
- Strong background in deep learning frameworks (e.g., TensorFlow, PyTorch, JAX).
- Proficiency in natural language processing (NLP), computer vision (CV), and speech processing techniques.
- Experience with model optimization techniques (e.g., quantization, pruning, distillation).
- Familiarity with distributed computing, in-memory computing platforms, or high-performance computing.
- Knowledge: A strong understanding of the latest advancements in AI/ML research, particularly in generative models (e.g. transformers and diffusion models).
- Collaboration & Communication: Ability to work in a highly collaborative, fast-paced startup environment and communicate complex technical concepts clearly.
Preferred Qualifications
- PhD or advanced degree in Computer Science, Machine Learning, AI, or related fields.
- 5+ years of post-graduation relevant work experience.
- Experience in deploying models on edge devices or in-memory computing systems.
- Familiarity with model deployment frameworks like TensorRT, ONNX, or similar.
- A passion for solving real-world challenges with AI in dynamic, high-performance environments.