Discover International
Machine Learning Engineer
Discover International, Boston, Massachusetts, us, 02298
Machine Learning Engineer
Location:
Boston, MA Delighted to be working with a client who are looking for Machine Learning Engineers specializing in Reinforcement Learning with Human Feedback (RLHF). Collaborate with cross-disciplinary experts in AI to build and refine cutting-edge models that drive innovation in therapeutic discovery and development. The Role Develop ML driven methods to enhance reasoning, planning, and decision-making in scientific research. Train and fine-tune models on scientific data, including incorporating RL with LLMs for advanced capabilities. Design robust evaluation frameworks and custom benchmarks to test performance and reliability. Develop and deploy machine learning models/LLM's for biological data. Requirements PhD in Computer Science, Machine Learning, Robotics, or a related field. Expertise in reinforcement learning, including policy optimization, model-based RL, or multi-agent settings. Experience with distributed computing platforms like AWS, GCP, or Azure. A strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR, AAAI). Familiarity with LLM training and fine-tuning methods is a plus.
Boston, MA Delighted to be working with a client who are looking for Machine Learning Engineers specializing in Reinforcement Learning with Human Feedback (RLHF). Collaborate with cross-disciplinary experts in AI to build and refine cutting-edge models that drive innovation in therapeutic discovery and development. The Role Develop ML driven methods to enhance reasoning, planning, and decision-making in scientific research. Train and fine-tune models on scientific data, including incorporating RL with LLMs for advanced capabilities. Design robust evaluation frameworks and custom benchmarks to test performance and reliability. Develop and deploy machine learning models/LLM's for biological data. Requirements PhD in Computer Science, Machine Learning, Robotics, or a related field. Expertise in reinforcement learning, including policy optimization, model-based RL, or multi-agent settings. Experience with distributed computing platforms like AWS, GCP, or Azure. A strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR, AAAI). Familiarity with LLM training and fine-tuning methods is a plus.