We are seeking a highly skilled LLM Research Engineer IV to join an advanced AI research team in Mountain View, CA. In this role, you will drive the next generation of generative AI by designing, training, and fine-tuning state-of-the-art Large Language Models (LLMs). You will bridge the gap between cutting-edge NLP research and scalable, production-ready AI systems.Key Responsibilities
- Model Development & Fine-Tuning: Design, train, and fine-tune large language models (e.g., GPT, LLaMA, PaLM) and explore advancements in transformer architectures and multi-modal models.
- Data Engineering: Collect, clean, and preprocess large-scale text datasets while ensuring data is free from bias and aligned with ethical AI standards.
- Optimization & Inference: Optimize model architecture to reduce latency, memory footprint, and inference time for real-time applications.
- Deployment & MLOps: Collaborate with MLOps teams to deploy LLMs into production environments using Docker, Kubernetes, and cloud infrastructure.
- Evaluation & Testing: Develop robust evaluation pipelines using metrics like perplexity, BLEU, and F1 score, and continuously test for bias, fairness, and robustness.
- Innovation & Research: Stay updated with the latest generative AI research, contribute to research papers/patents, and present findings at internal sessions and conferences.
Required Qualifications
- Education & Experience:
- Master’s degree in Computer Science, AI, or Data Science with 3+ years of post-graduation experience OR
- Bachelor’s degree in a related field with 5+ years of post-graduation experience.
- Core Tech Stack: Advanced proficiency in PyTorch, TensorFlow, or JAX, and hands-on experience with Hugging Face libraries and OpenAI APIs.
- Domain Expertise: Deep understanding of transformer-based models, sequence-to-sequence models, and advanced NLP techniques.
- Infrastructure & Compute: Strong understanding of distributed computing, GPU acceleration using CUDA, and MLOps tools (Docker, Kubernetes, CI/CD).
- Advanced AI (Plus): Knowledge of Reinforcement Learning and RLHF (Reinforcement Learning with Human Feedback) is highly desired.
Pay: $120.00 - $125.00 per hour
Benefits:
- Employee stock purchase plan
- Paid parental leave
- Stock options
Work Location: In person
