Artificial Intelligence Intern

About the Role

We record people teleoperating a robot arm through pick-and-place tasks and train policies on that video. Between recording and training sits a pipeline that nobody sees and everybody depends on — it filters out blurred, blown-out, occluded, or frozen frames, locates the end-effector, crops around it, and produces clean training clips with a label showing how much of each recording survived.

That pipeline is this internship. It's unglamorous and it's the highest-leverage part of the project: a policy trained on a frozen stream doesn't fail loudly — it quietly learns that the world doesn't move when you act on it.

Responsibilities

• Own the QC stage — sharpness, exposure, occlusion, near-duplicate, and region-of-interest checks on raw frames, with thresholds you can defend from the data
• Turn raw recordings into training clips: extraction, cropping, resizing, and keeping the crop stable across a clip rather than letting it jitter
• Decide which recordings are unusable and say so — a rejected clip is worth more than one quietly passed
• Use a VLM for checks a metric can't express (did the grasp happen, does the scene match its task label) — and know when it's the wrong tool
• Audit labels, sample by hand, and measure your own pipeline against what you find
• Work with Claude daily, and know exactly where you stop trusting it

Requirements

• Hands-on comfort with OpenCV, PIL, and numpy on your own images — not just benchmark data
• You look at the distribution before picking a threshold
• You've called a vision-language model yourself and have a sense of what it costs
• You check things — if a script says 53 frames passed, you open a few and look
• You can say what you didn't verify, without being asked twice
• Final-year student or recent graduate, able to work full-time from Bengaluru for 6 months

Nice to Have

• Robot learning or imitation-learning datasets (LeRobot, Open X-Embodiment)
• FFmpeg
• Off-the-shelf detection/segmentation on your own data
• Annotation QA
• Large-scale dataset tooling
• Kaggle, open-source work, or write-ups

Why This Is Worth Your Time

You'll own one stage of a real pipeline end to end and watch it decide what a robot learns. It's a small team, so nothing you build sits behind three layers of approval — and data quality is a skill that transfers to every ML role you'll go on to hold.