My client is an early stage technology company innovating on the application of AI to life sciences. They are looking for talented and creative Machine Learning Researchers to advance the development of state-of-the-art large language models (LLMs) within a dynamic and cross-functional team. The role involves designing, training, and fine-tuning large-scale models, focusing on domain-specific applications, multimodal data integration, and innovative training methodologies such as RLHF/DPO. The ideal candidate has experience building large-scale generative models.

Key Responsibilities

• Develop and Enhance LLMs: Design, train, and fine-tune large language models for domain-specific tasks, with an emphasis on innovative adaptation and problem-solving.
• Cross-Disciplinary Collaboration: Work closely with biologists, bioinformaticians, software engineers, and other specialists to create models that transform scientific research workflows.
• Build Robust NLP Solutions: Utilize NLP tools to tackle complex, real-world challenges, including sequential decision-making tasks.
• Implement and Maintain ML Pipelines: Establish comprehensive pre-processing, training, and evaluation pipelines, with an emphasis on model benchmarking, testing, and documentation.
• Research and Innovate: Stay up-to-date with cutting-edge research in NLP and LLMs, contributing new ideas to model development and deployment.

Qualifications

• Advanced Degree: PhD in computer science, applied mathematics, physics, computational biology, or a similar quantitative field.
• Deep Learning Experience: At least 4 years of experience developing deep learning or NLP models, particularly in building and scaling generative models.
• Publication Record: Contributions to recognized conferences or journals (e.g., NeurIPS, ICML, AAAI, ICLR) are highly valued.
• Technical Proficiency: Expertise in at least one major ML framework (e.g., PyTorch, TensorFlow, Jax) and a strong command of the Python data science ecosystem.
• Distributed Computing: Experience training and deploying models on distributed computing services (e.g., AWS, GCP, Azure, or on-prem clusters).

Additional Skills (Preferred):

• Knowledge of knowledge graphs, prompting strategies (e.g., Chain-of-Thought), retrieval-augmented generation (RAG), and autonomous agents.
• Familiarity with incorporating ML models into AI-driven scientific workflows.