Summary

As Head of AI (R&D), you will lead cross-functional teams to drive innovation in training multimodal large language models (LLMs), video foundation models, and other advanced AI systems. These models will be optimized for both cloud-based deployments and edge computing environments, ensuring scalability, efficiency, and real-world applicability. You will report directly to senior leadership and play a pivotal role in shaping the company's AI strategy, from research breakthroughs to production-ready solutions.

Key Responsibilities

1. Lead and mentor interdisciplinary teams of researchers, engineers, and data scientists to pioneer cutting-edge AI research and development.
2. Innovate on model architectures, training methodologies, and optimization techniques for multimodal LLMs (integrating text, image, audio, and video) and specialized video foundation models.
3. Scale the R&D team by recruiting top talent, fostering a collaborative culture, and implementing processes for efficient growth and knowledge sharing.
4. Oversee scalable training pipelines, including data curation, distributed computing strategies (e.g., using HPC clusters or cloud resources), and hyperparameter optimization to handle massive datasets.
5. Deliver high-quality, general-purpose models tailored to specific applications and use cases, ensuring they meet performance benchmarks for accuracy, latency, and resource efficiency on cloud and edge devices.
6. Collaborate with product, engineering, and deployment teams to transition research prototypes into production systems.
7. Stay abreast of industry trends, publish research findings, and represent the company at conferences or in partnerships.
8. Manage budgets, timelines, and resources for R&D projects, ensuring alignment with business objectives and pragmatic AI practices.

Job requirements

1. Proven experience leading AI R&D teams (5+ years in a leadership role) with a track record of delivering production-grade models from concept to deployment.
2. PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
3. Deep expertise in training foundation models, including multimodal LLMs and video models, with hands-on experience in frameworks like PyTorch, TensorFlow, or JAX.
4. Strong understanding of scalable training techniques, such as data parallelism, model parallelism, and efficient inference on edge devices (e.g., mobile, IoT).
5. Demonstrated ability to scale teams and projects in fast-paced environments.

Preferred

1. Publications in top-tier conferences/journals (e.g., NeurIPS, CVPR, ICML) on relevant topics.
2. Experience with real-world deployments of AI models in datacenter and edge settings.
3. Familiarity with ethical AI, bias mitigation, and regulatory compliance in model development.
4. Strong programming skills in Python and experience with distributed systems (e.g., Kubernetes, Ray).

Skills

• Compliance Knowledge
• Development
• Machine Learning
• Python