The Company We are recruiting for an organization at the forefront of developing groundbreaking technology in electric vehicles, autonomous driving, and humanoid robotics. As they expand their efforts in AI, we are seeking passionate engineers at all levels to join the team and contribute to the development of advanced large language models and foundation models. You will be part of a dynamic, collaborative team where your impact will be felt across every product and project, influencing the future of AI and robotics.
Key Responsibilities
- Fine-tune pre-trained large language models for specialized use cases, including humanoid robots and autonomous vehicles (e.g., conversational AI, API integrations, etc.).
- Develop efficient LLMs (e.g., compact models, weight sharing, model quantization) suitable for local deployment in robots and vehicles.
- Build and maintain scalable LLM training and deployment pipelines to support high-volume production environments.
- Utilize large-scale datasets and computing resources effectively to improve model performance and efficiency.
- Explore innovative approaches to generate synthetic data that enhances data diversity and quality.
- Collaborate closely with cross-functional teams to ensure alignment on product requirements and AI-related objectives.
- Ph.D. in Computer Science or a related field.
- Strong expertise in machine learning, natural language processing (NLP), computer vision, speech processing, or data science.
- Experience working with large language models (LLMs).
- Proficient in Python programming and software development, with extensive experience in a major deep learning framework.
- Excellent communication skills and a collaborative mindset.
- In-depth understanding of modern language model architectures, LLM training processes, and techniques such as model quantization.
- Familiarity with technologies related to LLMs, including supervised fine-tuning (SFT), reinforcement learning with human feedback (RLHF), prompt engineering, and retrieval-augmented generation (RAG).
- A proven track record of publications, innovations, or leadership in the field of AI.
