What are the responsibilities and job description for the LLM & RL Engineer / Agents Engineering position at YC Bench?
Company Description
YC Bench is a live benchmarking platform that evaluates and ranks forecasting models to predict the success of Y Combinator startups compared to their cohort peers. Our mission is to bring transparency and analytics to the startup ecosystem, enabling data-driven decisions. By leveraging cutting-edge machine learning and AI, we aim to set the gold standard for assessing startup performance potential. Join us to make an impact on how innovation thrives in early-stage ventures.
Role Description
This is a full-time remote role for a LLM & Reinforcement Learning Engineer specializing in Agents Engineering. The role involves designing, developing, and deploying cutting-edge large language models (LLMs) and reinforcement learning (RL)-based agents. Day-to-day activities include implementing algorithms, fine-tuning pre-trained models, integrating AI solutions into scalable systems, performing rigorous evaluations, and collaborating with cross-functional teams to address core machine learning challenges. You will support the innovation cycle, from ideation to execution, and contribute to improving overall model performance for benchmarking solutions.
Qualifications
YC Bench is a live benchmarking platform that evaluates and ranks forecasting models to predict the success of Y Combinator startups compared to their cohort peers. Our mission is to bring transparency and analytics to the startup ecosystem, enabling data-driven decisions. By leveraging cutting-edge machine learning and AI, we aim to set the gold standard for assessing startup performance potential. Join us to make an impact on how innovation thrives in early-stage ventures.
Role Description
This is a full-time remote role for a LLM & Reinforcement Learning Engineer specializing in Agents Engineering. The role involves designing, developing, and deploying cutting-edge large language models (LLMs) and reinforcement learning (RL)-based agents. Day-to-day activities include implementing algorithms, fine-tuning pre-trained models, integrating AI solutions into scalable systems, performing rigorous evaluations, and collaborating with cross-functional teams to address core machine learning challenges. You will support the innovation cycle, from ideation to execution, and contribute to improving overall model performance for benchmarking solutions.
Qualifications
- Proficiency in designing, implementing, and fine-tuning Large Language Models (LLMs) and expertise in Reinforcement Learning algorithms.
- Experience with training deep learning models, neural networks, and frameworks like TensorFlow or PyTorch.
- Strong background in Python and familiarity with related data science libraries such as NumPy, pandas, and Scikit-learn.
- Experience with software engineering best practices, including version control (Git), containerization (Docker), and deployment pipelines.
- Familiarity with agent-based systems and AI integrations within software solutions is a plus.
- Proven experience with quantitative research, data analysis, and model evaluation to drive data-informed decisions.
- Excellent problem-solving skills, critical thinking, and the ability to work collaboratively in cross-functional teams.
- Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field is preferred.