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Salary
≈ $21k – $37k per year (Estimated)
Location
In office (Singapore)
Seniority
Intern
Employment
Internship

Confirmed on the employer's own hiring board on Oct 30, 2026. First seen by Alion on Oct 28, 2026.

Overview
Company
Impact
Profile match
Headquartered in Singapore, LionsBot is a smart robotics company specializing in autonomous floor-cleaning solutions for the commercial cleaning industry. The firm develops and manufactures AI-driven cleaning robots powered by its proprietary cloud operating system to automate large-scale sanitation across commercial and industrial facilities. Combining hardware engineering with intelligent software, its technology helps facility operators improve cleaning efficiency, optimize workflows, and support workplace health standards.

Overview

The intern will support development of the data flywheel pipeline powering our robotics fleet's AI capabilities, including a prototype query system for dataset retrieval and collation. They will also design and run 2D detection and segmentation experiments, delivering benchmarking results and recommendations to the team.

Key Responsibilities:

KR1: Support the building of the data flywheel pipeline that powers the AI capabilities of our robotics fleet.

KR2: Build a smart database query system for fast dataset retrieval and collation

KR3: Designing and running visual model experiments for 2D detection and segmentation pipeline, with results provided back to the team to act on.

Deliverables:

(from KR2) A working prototype of a smart database query system.

(from KR3) Provide sets of benchmarking results for vision model experiments, with clear findings and recommendations for the production stack.

Learning Objectives:

State what the intern should be able to do by the end of the placement. These should be specific and assessable - they will also form the basis of the mid-point and final review.

See the full data flywheel in motion: collection, curation, labelling, QA, and retraining at fleet scale.

Train and benchmark modern vision models on real-world data from robots in live commercial environments.

Put vision-language models to work and automate what used to be manual annotation effort.

Build the engineering habits that separate research code from shipped code (reproducibility, versioning, clean experiments).

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