Machine Learning Engineer - Model Evaluation & Experimentation

• 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.

• 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.

• →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.

We are committed to fostering an inclusive and diverse environment where all qualified applicants receive equal consideration. Reasonable accommodations are available throughout the application and engagement process.

• Independent contractor engagement.
• Fully remote with flexible working hours.
• Expected commitment of approximately 35 hours per week.
• Project duration may be extended, shortened, or concluded based on project requirements and individual performance.
• Work does not require access to confidential or proprietary information from any current or former employer.
• Payments are issued weekly based on approved work completed.
• At this time, we are unable to support H1-B or STEM OPT candidates.