AI/ML Research Engineer

About This Role

This AI/ML Research Engineer will push the boundaries of generative AI models, focusing on developing cutting-edge algorithms and applications to enhance state-of-the-art LLMs. The role involves designing, implementing, and validating new algorithms for augmented LLMs and collaborating with multidisciplinary teams to build scalable, efficient, and innovative generative AI systems.

Responsibilities

• Conduct high-quality research in generative AI, including designing algorithms for pre-training and post-training current autoregressive and diffusion models for multimodal data
• Design, implement, and validate new algorithms and models for augmented LLMs, pushing the boundaries of AI capabilities
• Develop and prototype novel algorithms for fine-tuning, retrieval augmented generation, and in-context learning for various generative models
• Develop algorithms for training and inference in Energy-Based Models
• Collaborate with cross-functional teams to apply research findings to develop new products or enhance existing ones
• Publish research papers in top-tier journals and conferences, sharing findings with the broader scientific community
• Stay abreast of the latest AI research and trends, identifying opportunities for innovation and improvement
• Mentor junior researchers and engineers, fostering a culture of knowledge sharing and collaboration
• Develop prototypes and proof-of-concept implementations to demonstrate the potential of research findings
• Engage with the academic community by attending conferences, workshops, and seminars

Requirements

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Physics, Mathematics, or other related fields
• 3+ years working experience with training and fine-tuning generative AI models including LLMs, diffusion models, or Energy-Based Models
• Experience with test-time compute techniques, such as chain-of-thoughts, self-consistency, or reinforcement learning based verifiers
• Proven track record of research in generative models, demonstrated through first tier publications (e.g., NeurIPS, ICML, ICLR, or high impact journals), patents, or publicly available projects
• Proficiency in programming languages commonly used in AI research, such as Python
• Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch)
• Deep understanding of machine learning algorithms and principles, especially in the context of generative AI
• Strong mathematical background, with excellent skills in areas such as statistics, probability, linear algebra
• Creative and analytical thinking abilities, with a passion for solving complex problems
• Excellent communication skills

Qualifications

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Physics, Mathematics, or other related fields
• 3+ years working experience with training and fine-tuning generative AI models including LLMs, diffusion models, or Energy-Based Models

Nice to Have

• Knowledge of computational physics, statistical physics, many-body physics simulations, or quantum computing
• Proficiency with hosting and operating locally generative AI models, through frameworks such as ollama, vllm
• Background in discrete optimization, combinatorial optimization, Monte Carlo methods, quantum-inspired algorithms
• Programming experience in high-performance computing (HPC) environments
• Experience with cloud computing platforms, GPU computing, or FPGA computing

Skills

* Required skills

Benefits