ML Benchmark Researcher

Author and validate research-grade machine-learning benchmarks in areas where you write code and publish results. 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 NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, CoRL 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. #J-18808-Ljbffr