ML Benchmark Researcher

What you will do

• Build realistic research tasks, reference solutions and grading rubrics.
• Validate whether benchmarks measure the intended capability and discriminate between correct and weak approaches.
• Contribute depth in areas such as language models, deep learning, RL, vision/generation, robotics, efficient systems, optimization/theory, classical or adaptive learning, time series, causal reasoning, trustworthy learning or AI for science.

Required background

• At least one year of hands-on ML research, with no upper experience limit.
• At least one first-author ML paper or paper in a closely related field.
• Completed or ongoing master’s/doctoral study in a quantitative subject such as machine learning, computer science, statistics or mathematics.
• Practical ML coding in training, evaluation or systems; annotation alone does not meet this research background.

Preferred background

• Multiple first-author papers or results demonstrating measured improvement over a baseline.
• Publications at venues including Neur

  IPS, ICML, ICLR, CVPR, ACL, EMNLP, Co

  RL or MLSys.
• Deep expertise in an applicable research area.

Location and eligibility

• Remote work; country restrictions are not stated.

Availability

• Flexible remote research work, approximately 5–40 hours per week.

Pay and engagement

• $158/hr USD hourly compensation. The applicable rate and agreed work scope are confirmed before work begins.