[Remote] Machine Learning Engineer - AI Trainer
Note: The job is a remote job and is open to candidates in USA. DataAnnotation develops AI systems and offers flexible, remote work opportunities for quantitative professionals. The Machine Learning Engineer - AI Trainer will evaluate AI-generated quantitative analyses, design problems for training and benchmarking AI systems, write technical explanations and analytical code, and provide feedback to improve AI reasoning models.
Responsibilities
- Evaluate AI-generated quantitative work, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, for technical accuracy and real-world validity
- Design and solve quantitative problems used to train and benchmark AI systems, spanning areas like forecasting, experimental analysis, optimization, and statistical inference
- Write clear technical explanations and well-documented analytical code
- Provide feedback that directly shapes the next generation of AI models built for quantitative reasoning
Skills
- 2+ years of hands-on experience in a quantitative role or research environment — such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or any adjacent field
- Some coding experience required, with comfort writing and reviewing analytical code end-to-end
- Practical experience with statistical methods, predictive modeling, and experiment design (e.g., A/B testing, hypothesis testing, regression, classification, time-series forecasting)
- Fluency in English (native or bilingual level) with strong writing skills
- A bachelor's degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar); a master's or PhD is a plus
- Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or equivalent demonstrated expertise)
Benefits
- Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand.
- Flexible schedule: choose which projects you take on and when you work.
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