AI Research Engineer

2 weeks ago

California, United States Krutrim Full time

About Krutrim:

Krutrim is at the forefront of AI computing innovation. Our vision encompasses a comprehensive AI computing stack that includes AI infrastructure, AI Cloud services, multilingual and multimodal foundational models, and AI-driven applications. As India's pioneering AI unicorn, we have developed the first foundational model in the country.

Our AI ecosystem empowers consumers, startups, enterprises, and researchers globally to create their own AI applications or models. We are focused on developing foundational models across text, voice, and vision, while also creating AI training and inference platforms that facilitate research and development across various industry sectors. The solutions we are building at Krutrim aim to positively impact millions of lives across diverse income and education levels, as well as across multiple languages.

The Krutrim team is a blend of expertise in AI research, Applied AI, Cloud Engineering, and semiconductor design, operating from multiple locations.

Job Overview:

We are seeking skilled Generative AI Engineers to train, optimize, scale, and deploy a range of generative AI models, including large language models, voice/speech foundation models, and vision/multimodal foundation models using state-of-the-art techniques and frameworks. In this position, you will engage in advanced research and development to explore the limits of generative AI and language models.

Key Responsibilities:

1. Conduct research, design, and implement new generative AI methodologies such as autoregressive models, causal models, and diffusion models.

2. Enhance foundational model infrastructure to support the deployment of optimized AI models, focusing on C/C++, CUDA, and kernel-level programming improvements.

3. Apply cutting-edge optimization techniques, including quantization, distillation, sparsity, streaming, and caching, to enhance model performance.

4. Train or fine-tune vision models for various tasks including representation (e.g., Vision Transformers, Q-Former, CLIP), generation (e.g., Stable diffusion), and video representation (e.g., Video-Swin transformer).

5. Innovate and develop novel large language models and architectures utilizing transformers, Mixture-of-experts, and advanced attention mechanisms.

6. Implement large multimodal models based on the latest architectural advancements.

7. Experience with audio models for representation and generation is advantageous.

8. Drive innovation in state-of-the-art architectures for tasks such as Panoptic Segmentation, Image Classification, and Image Generation.

9. Engage in AI Arts, Image Prompts, and Conditional Image Generation as an additional advantage.

10. Advance NLP techniques including text generation, summarization, translation, and question answering through generative models.

11. Integrate and customize frameworks like PyTorch, TensorFlow, and others for rapid model training and inference.

12. Enhance deployment infrastructure using MLOps frameworks, ensuring efficient development and deployment processes.

13. Contribute to the academic community by publishing research at leading AI/ML conferences.

14. Collaborate with engineering teams to translate research advancements into scalable services and products.

Qualifications:

1. Ph.D. or MS with a minimum of 2 years of research or applied research experience in LLMs, NLP, CV, Reinforcement Learning, Voice, and Generative models.

2. Proven expertise in high-performance computing with strong programming skills in Python, C/C++, CUDA, and kernel-level programming for AI applications.

3. Extensive experience in optimizing training and inference for large-scale AI models, including knowledge of quantization and distillation.

4. Prior experience with large-scale distributed training and fine-tuning of foundational models.

5. Familiarity with language modeling evaluation, prompt tuning, and instruction tuning.

6. Demonstrated research contributions in NLP and generative modeling through publications and practical applications.

7. Strong programming capabilities and proficiency in various ML frameworks and tools.

8. Experience in Information Extraction, Question Answering, and Conversational Agents is a plus.

9. Excellent communication and collaboration skills to work effectively across teams.

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