Demo

Large Language Model Machine Learning Researcher

Alldus
Boston, MA Full Time
POSTED ON 2/25/2025
AVAILABLE BEFORE 5/21/2025

Join an innovative early-stage technology company at the forefront of applying AI to life sciences. We are seeking talented and imaginative Machine Learning Researchers who are passionate about advancing the development of state-of-the-art large language models (LLMs) as part of a dynamic, cross-functional team. In this role, you will design, train, and fine-tune large-scale models, emphasizing domain-specific applications and innovative training methodologies such as RLHF / DPO.

Key Responsibilities

  • Develop and Enhance LLMs : Lead the design, training, and fine-tuning of large language models tailored to domain-specific tasks, focusing on innovative problem-solving and adaptation strategies.
  • Cross-Disciplinary Collaboration : Collaborate closely with biologists, bioinformaticians, software engineers, and other specialists to develop transformative models that enhance scientific research workflows.
  • Build Robust NLP Solutions : Employ NLP tools to address complex, real-world challenges, including sequential decision-making tasks.
  • Implement and Maintain ML Pipelines : Construct comprehensive pre-processing, training, and evaluation pipelines with a strong focus on model benchmarking, testing, and thorough documentation.
  • Research and Innovate : Stay at the cutting edge of NLP and LLM research, contributing fresh ideas and insights to model development and deployment.

Qualifications

  • Advanced Degree : PhD in computer science, applied mathematics, physics, computational biology, or a closely related quantitative field.
  • Deep Learning Experience : Minimum of 4 years of experience in developing deep learning or NLP models, especially in building and scaling generative models.
  • Publication Record : Demonstrated contributions to renowned 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 strong command of the Python data science ecosystem.
  • Distributed Computing : Experience in training and deploying models on distributed computing services (e.g., AWS, GCP, Azure, or on-prem clusters).
  • Additional Skills (Preferred) :

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