We are seeking a highly skilled LLM Researcher to advance innovation in Large Language Models (LLMs), foundation model development, and scalable AI deployment. The ideal candidate will bring significant hands-on experience from related fields such as Natural Language Processing (NLP), deep learning, transformer architectures, foundation model pretraining, and scalable model deployment, combined with an active interest in shaping next-generation LLM solutions.
Core Responsibilities
- R&D: Design and experiment around LLM architectures, transformer-based models, optimization methods (e.g., parameter-efficient finetuning, quantization), and pretraining/fine-tuning pipelines to improve language understanding and generation tasks.
- Data Engineering: Develop robust, scalable data pipelines for multi-modal datasets; manage preprocessing, cleaning, and curation workflows, essential for efficient LLM training and inference.
- Model Development & Evaluation: Lead development and evaluation of LLMs, leveraging experience in transformers, self-supervised learning, and large-scale distributed training. Optimize training pipelines for performance, compute efficiency, and responsible AI alignment.
- LLM Deployment & Scalability: Own the end-to-end deployment of LLMs and foundation models into production, focusing on system integration, inference, and optimization.
- Cross-functional Collaboration: Engage with research scientists, ML engineers, and product teams to align technical advances with business needs, contributing to both research and applied AI projects. Provide mentorship and technical guidance to junior researchers and engineers, fostering best practices in LLM development, scalable model deployment, and model evaluation.
Qualifications
- Experience: 3+ years of proven experience in NLP, transformer models, deep learning, scalable AI pipelines with proven involvement in LLM development, fine-tuning, or deployment in recent projects.
- Technical Skills:
- Problem-solving: Strong analytical skills to troubleshoot model performance and data pipeline bottlenecks.
- Proficiency in Python and frameworks like PyTorch, TensorFlow, or JAX.
- Expertise in distributed computing and data engineering tools (e.g., SLURM, Apache Spark, Airflow).
- Hands-on experience with LLM training, fine-tuning, and deployment (e.g., Hugging Face, LLamafactory, NVIDIA NeMo).
- Preferred Qualification:
- Advanced degree (MS/PhD) in Computer Science, AI, or related field.
- Publications in top-tier conferences (main author).
- Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools.
Compensation and Benefits
We offer a competitive compensation package, private health insurance, and other perks. Our working environment is great and collaborative, fostering creativity and innovation.