Role Overview
We are looking for a logically-minded ML Field Intern to bridge the gap between our field trials and our machine learning models. You will be responsible for the full data cycle: capturing data on-site, processing that data through our ML models, and systematically logging the performance results. This role requires a decent level of software comfort and a strong ability to organize technical information.
Key Responsibilities
- On-Site Data Acquisition: Attend trials to capture raw data (images, sensor logs, etc.) and ensure the quality of the data collection process.
- ML Model Execution: Run our existing machine learning models using the collected trial data. You will be responsible for setting up the environment, feeding the data, and triggering the inference process.
- Technical Data Logging: Extract the outputs from the ML models and meticulously log the results into Excel/Google Sheets. You will track success rates, false positives, and specific logic errors.
- Trial Performance Reporting: Create concise summaries of how the model performed on-site compared to expectations, identifying specific scenarios where the logic needs improvement.
- Data Annotation: Label and prepare new datasets to help improve the model's accuracy based on what you observed during trials.
Required Skills & Qualifications
- Software Competency: You must be comfortable with running software (e.g., using a terminal/command prompt, managing file paths, and following technical SOPs).
- Logical Analysis: Ability to look at model outputs and identify if the result makes sense based on the physical trial conditions.
- Excel Proficiency: Strong skills in data entry and organization. You should be comfortable with formulas, filters, and structured logging in Excel.
- Attention to Detail: Meticulous about data integrity. You understand that a single wrong entry in a log sheet can ruin a month of research.
Preferred Skills
- Familiarity with Linux or basic Python commands.
- Experience in an industry involving hardware/software integration (e.g., Robotics, IoT, or Computer Vision).
- Previous experience in data-heavy roles or technical documentation.
