Machine Learning Engineer - Model Evaluation, Experimentation

Job Description:

• 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-is preferred.
• Experience with AI evaluation, benchmark development, AI training, or task authoring is highly desirable.
• 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.

Benefits: