ML Research Expert — Published First Author (Benchmark Authoring)

Position: ML Research Expert — Published First Author (Benchmark Authoring)

Type: Contract

Compensation: $140–$150/hour

Location: Remote

About the Opportunity

AfterQuery is seeking experienced Machine Learning Research Experts with published first-author research to design and validate benchmark tasks that measure what frontier AI models can actually accomplish across machine learning.

This opportunity is intended for researchers who write ML code, conduct experiments, and publish research results, rather than candidates with annotation or data-labeling experience. Selected contributors will develop realistic research tasks, expert-level reference solutions, and grading rubrics within their areas of deep technical expertise.

The work spans a broad range of machine learning and AI research domains, including language models, deep learning, reinforcement learning, computer vision, robotics, ML systems, optimization and theory, causal reasoning, trustworthy learning, and AI for science.

Responsibilities

- Design realistic and technically rigorous machine learning research tasks and problem sets within your area of expertise.
- Develop benchmark challenges that assess the ability of AI models to solve authentic ML research problems.
- Author expert-level reference solutions for assigned research tasks.
- Develop detailed grading rubrics that enable consistent evaluation of model performance.
- Apply hands-on machine learning research experience when designing and validating benchmark content.
- Ensure tasks are technically accurate, challenging, reproducible, and relevant to real ML research workflows.
- Validate research tasks and solutions against appropriate technical and methodological standards.

Required Qualifications





- At least one first-author research paper in machine learning or a closely related field.
- Master's or PhD degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related quantitative field, completed or currently in progress.
- At least 1 year of hands-on machine learning research experience.
- Practical experience writing machine learning code for training, evaluation, experimentation, or ML systems work.
- Demonstrated experience conducting research and reporting technical results.
- Robust expertise in at least one relevant machine learning research area.
- Ability to design and evaluate technically sophisticated ML research problems.
- Candidates must meet the specified experience and education requirements and provide the requested supporting materials.

Preferred Qualifications

- Multiple first-author machine learning publications.
- Research papers demonstrating measured improvements over established baselines.
- Publications at recognized ML/AI research venues, including:
- NeurIPS
- ICML
- ICLR
- CVPR
- ACL
- EMNLP
- CoRL
- MLSys
- Deep specialization in one or more of the listed research areas rather than broad familiarity across many areas.
- Experience developing research benchmarks, evaluation methodologies, or technical problem sets.
- Strong record of experimental machine learning research and reproducible technical work.

Key Areas of Expertise

- Language Models




- Deep Learning
- Reinforcement Learning
- Computer Vision
- Generative AI
- Robotics
- ML Systems
- Efficient Machine Learning
- Optimization & Learning Theory
- Classical Machine Learning
- Adaptive Learning
- Time Series & Forecasting
- Structured Reasoning
- Causal Reasoning
- Trustworthy Machine Learning
- AI for Science

Compensation & Engagement

- Compensation: $140–$150/hour
- Engagement Type: Independent contract
- Work Arrangement: Fully remote and asynchronous
- Hours: Flexible commitment of approximately 5–40 hours per week
- Project Focus: Machine learning research benchmark authoring and AI evaluation
- Experience Level: 1+ years of hands-on ML research experience; no upper experience limit
- Candidates who meet the stated requirements and provide the requested materials may receive priority during the review process.
- The application process includes thorough background checks.

About AfterQuery AfterQuery is a research lab investigating the boundaries of artificial intelligence through novel datasets and experimentation.

The company is backed by leading investors, including Y Combinator and Box Group, and supports AI research across leading AI laboratories.

Application Process

1. Submit your application with your resume and requested research materials.
2. Provide evidence of at least one first-author ML research publication.
3. Demonstrate your relevant ML research experience, education, and hands-on coding background.
4. Complete any required technical or research assessment.
5. Complete the applicable background-check process.
6. Complete onboarding and project orientation if selected.
7. Begin authoring and validating machine learning benchmark tasks.

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