AI Intern

Full Description

Thingtrax is building an Agentic Manufacturing Operations platform to transform how factories operate.

By deploying AI-powered agents, including advanced computer vision systems, directly on production lines, we help manufacturing operations observe, reason, and act in real time.

Our mission focuses on collaborating closely with manufacturers, particularly in the food and beverage sectors, to enhance efficiency, reduce waste, and enable a shift from manual processes to autonomous, optimized operations.

Key Responsibilities Engage in a variety of tasks related to the detection, tracking, and segmentation of objects or features within datasets, demonstrating a keen ability to analyze and interpret complex data structures.

Take a proactive role in the development and evaluation of advanced artificial intelligence models, ensuring that they are not only effective but also align with the latest industry standards and practices.

Provide essential support for data preparation and annotation efforts, which involves meticulously organizing datasets and applying precise labeling techniques to ensure high-quality inputs for model training and validation.

Health Insurance (Coverage for Spouse and Children if Married; Coverage for both Parents if Single) Company-Sponsored Trips Quarterly Dinner Events Paid Time Off for Holidays Learning and Development Support - Course/exam fees covered after approval.

Gym Membership Allowance Fresh graduates / Final-year students (CS, IT, AI, Data Science).

Candidates must possess a solid foundation in Python programming, as this will be essential for developing and executing algorithms effectively.

A basic understanding of Computer Vision principles is required, as this knowledge will enable you to work with visual data analysis and interpret images meaningfully.

It is crucial for applicants to have a foundational grasp of object tracking , which plays a vital role in many applications in the field of computer vision.

Familiarity with segmentation techniques is also important, as it involves partitioning digital images into multiple segments to simplify their representation and analysis.

Moreover, having exposure to deep learning frameworks, particularly PyTorch , will be highly advantageous, as it is a preferred tool for implementing machine learning models that can learn from data efficiently.