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).
Work closely with cross-functional teams, including AI researchers, hardware engineers, and software engineers to integrate AI models into the broader platform.
Focus on optimizing models for memory efficiency, low-latency inference, and high throughput.
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.
Implement best practices for model testing, deployment, and continuous improvement to ensure models scale effectively in production environments.

Proven experience (for all levels) in developing and deploying multimodal models, including text, image, and/or audio data.
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.
A strong understanding of the latest advancements in AI/ML research, particularly in generative models (e.g. transformers and diffusion models).
Ability to work in a highly collaborative, fast-paced startup environment and communicate complex technical concepts clearly.

Impact: Work on groundbreaking technology that will power the next wave of AI applications, from edge computing to embodied AI systems.
Culture: Join a diverse, driven team that values innovation, collaboration, and continuous learning.
Growth: As a Series B startup, you’ll have significant growth opportunities, including the chance to shape the direction of the product and AI strategy.
Compensation: Competitive salary, equity options, and benefits package.

Senior Engineer – Multimodal AI Model Development Research

Key skills