Machine Learning Engineer - Model Evaluation & Experimentation

Weekday AI is a pioneering company focused on building next-generation evaluation benchmarks for frontier AI models. They are seeking experienced Machine Learning Engineers and Researchers to design and implement sophisticated machine learning challenges that establish high-quality evaluation benchmarks for advanced AI systems.

Responsibilities:

• Design realistic machine learning benchmark tasks based on research workflows, including model implementation, experimentation, training, evaluation, and performance analysis
• Translate open-ended research concepts into structured, reproducible evaluation tasks with clearly defined success criteria
• Implement machine learning solutions using Python, execute experiments, and produce reference implementations that demonstrate correct methodology and expected outcomes
• Develop benchmark tasks involving reinforcement learning concepts such as reward functions, policy optimization, training dynamics, and model behavior where applicable
• Evaluate AI-generated solutions by identifying implementation errors, experimental flaws, incorrect reasoning, and unsupported conclusions
• Collaborate with AI researchers and fellow subject matter experts to continuously improve benchmark quality, technical rigor, and evaluation consistency

Requirements:

• Master's degree, PhD, or equivalent practical experience in Machine Learning, Computer Science, Artificial Intelligence, Data Science, or another quantitative STEM discipline
• Minimum 1 year of professional experience in machine learning research, research engineering, applied AI, or another research-intensive technical role
• Strong hands-on experience designing, training, evaluating, and optimizing machine learning models through complete experimental workflows
• Practical experience conducting machine learning experiments, including experiment setup, hyperparameter tuning, execution, validation, and analysis
• Strong understanding of modern Large Language Models (LLMs), their capabilities, limitations, and evaluation methodologies
• Proficiency in Python and Git, with experience working in both script-based and notebook-based development environments
• Familiarity with reinforcement learning concepts—including reward functions, policy optimization, and training behavior
• Excellent analytical thinking, creativity, attention to detail, and the ability to solve complex, open-ended technical problems independently
• Strong written communication skills for documenting experimental methodologies and technical findings
• Ability to commit approximately 35 hours per week on a consistent basis
• Experience developing or evaluating large language models, foundation models, or generative AI systems
• Background in reinforcement learning, deep learning, distributed training, or model optimization
• Familiarity with benchmark design, AI safety evaluations, or research-quality experimentation
• Experience contributing to research publications, open-source machine learning projects, or advanced AI systems