AI Research Engineer

Role Overview FlashIntel is seeking a dedicated and innovative Research Engineer with a focus on Multimodal Large Language Models (m-LLMs), Text-to-Speech (TTS) technologies, and agentic workflows. This position offers a unique opportunity to engage in cutting-edge research and development of AI solutions that integrate various data modalities and enhance interactive systems. Ideal candidates are passionate about AI and Natural Language Processing (NLP) and eager to contribute to pioneering projects. Candidates pursuing a Master's or Ph.D. in relevant fields are preferred.​

Key Responsibilities
Research & Development:
Assist in developing state-of-the-art m-LLMs and generative AI models that process and integrate multiple data modalities, including text, audio, and visual inputs.​
Design and conduct experiments to test new algorithms, architectures, and fine-tuning techniques for TTS applications and agentic workflows.​
Stay updated with the latest academic and industry advancements in m-LLMs, TTS, and AI agents to inform ongoing projects.​
Model Design & Optimization:
Participate in developing, fine-tuning, and evaluating AI models for complex NLP tasks, with a focus on TTS and multimodal integration.​
Optimize models for scalability, efficiency, and deployment in real-world production systems, ensuring seamless interaction between components in agentic workflows.​
Experiment with novel methods in model training, domain adaptation, and performance evaluation to enhance the naturalness and responsiveness of TTS systems.​
Collaboration & Learning:
Work closely with cross-functional teams, including product, engineering, and data science, to translate research insights into tangible products that utilize m-LLMs and TTS technologies within agentic workflows.​
Engage in a culture of learning and innovation, contributing to team knowledge sharing on m-LLMs, TTS, and AI agents.​
Collaborate with external research communities, potentially contributing to conferences and publications in the fields of m-LLMs, TTS, and agentic AI systems.​

Required Qualifications
Educational Background:
Currently pursuing an advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, NLP, or a closely related field.​
Technical Expertise:
Experience or coursework in large language models, deep learning, NLP, and TTS technologies.​
Proficiency in programming languages such as Python, with experience in frameworks like TensorFlow or PyTorch.​
Understanding of algorithm design, data structures, and model optimization techniques relevant to m-LLMs and TTS systems.​
Professional Skills:
Ability to develop and deploy machine learning models in practical environments, with a focus on TTS applications and agentic workflows.​
Strong analytical and problem-solving skills, with the capacity to work both independently and collaboratively.​
Effective written and verbal communication skills for articulating research concepts to diverse audiences.​

Preferred Qualifications
Advanced Research Experience:
Progress toward a Ph.D. in a relevant discipline with a record of publications or patents.​
Experience in pioneering research in AI, with familiarity in m-LLM frameworks and toolkits (e.g., Hugging Face Transformers).​
Specialized Skills:
Experience with TTS systems, including speech synthesis and voice conversion technologies.​
Familiarity with agentic workflows and the integration of AI agents in interactive systems.​
Experience with distributed systems and scalable model deployment in cloud environments.​

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