Subham Banik

Key Skills

Software

Labelbox

Playment

CVAT

AWS SageMaker

SuperAnnotate

Top Subject Matter

Autonomous Vehicles

Railway Infrastructure

Autonomous Driving

Top Data Types

3D Sensor

Text

Document

Audio

Video

Image

Top Task Types

Object Detection

Entity Ner Classification

Bounding Box

Classification

Segmentation

Tracking

Freelancer Overview

Data Annotation Engineer (Data Annotation Projects). Core strengths include Internal, Proprietary Tooling, and Labelbox. Education includes Bachelor of Technology, Camellia School of Engineering and Technology (2020). AI-training focus includes data types such as 3D Sensor, Text, and Document and labeling workflows including Object Detection, Entity (NER) Classification, and Bounding Box.

Labeling Experience

Data Annotation Engineer (Data Annotation Projects)

3D SensorObject Detection

Labeled LiDAR datasets for 3D machine learning, contributing to improved model precision for railway applications. Annotated 2D LiDAR images to enhance quality in tamping assistance and supported the infrastructure of railway industry datasets. Led validation processes and ensured continuous performance improvements through high-quality data. • Labeled extensive LiDAR scenes for 3D ML models • Annotated 2D LiDAR images for tamping optimization • Used IAAS Tool to deliver accurate datasets • Achieved 98% client satisfaction with annotation quality.

Labeled LiDAR datasets for 3D machine learning, contributing to improved model precision for railway applications. Annotated 2D LiDAR images to enhance quality in tamping assistance and supported the infrastructure of railway industry datasets. Led validation processes and ensured continuous performance improvements through high-quality data. • Labeled extensive LiDAR scenes for 3D ML models • Annotated 2D LiDAR images for tamping optimization • Used IAAS Tool to deliver accurate datasets • Achieved 98% client satisfaction with annotation quality.

2025 - Present

Executive Delivery (Data Annotation Projects)

Labelbox3D SensorObject Detection

Curated and validated datasets for machine learning projects spanning autonomous driving and healthcare domains. Executed annotation workflows, integrated automation, and ensured rigorous quality assurance for client deliverables. Led project definition with clients and supported large annotation teams on delivery. • Managed 50+ annotation projects to on-time completion • Delivered datasets (LiDAR, NLP) with high client satisfaction • Reduced manual error via annotation automation • Defined and enforced annotation guidelines in global teams.

Curated and validated datasets for machine learning projects spanning autonomous driving and healthcare domains. Executed annotation workflows, integrated automation, and ensured rigorous quality assurance for client deliverables. Led project definition with clients and supported large annotation teams on delivery. • Managed 50+ annotation projects to on-time completion • Delivered datasets (LiDAR, NLP) with high client satisfaction • Reduced manual error via annotation automation • Defined and enforced annotation guidelines in global teams.

2023 - 2024

Operational Team Lead (Data Annotation Projects)

Playment3D SensorObject Detection

Oversaw the annotation operations for large-scale LiDAR datasets in the autonomous vehicle industry. Led a sizable annotation team, ensuring high data quality through SOP development and adherence to service level agreements. Trained annotators and optimized workflows to increase throughput and quality. • Managed 25-member team on LiDAR annotation projects • Achieved 97.5% quality and SLA compliance • Developed SOPs and benchmarks to streamline output • Trained new annotators and reduced rework rates.

Oversaw the annotation operations for large-scale LiDAR datasets in the autonomous vehicle industry. Led a sizable annotation team, ensuring high data quality through SOP development and adherence to service level agreements. Trained annotators and optimized workflows to increase throughput and quality. • Managed 25-member team on LiDAR annotation projects • Achieved 97.5% quality and SLA compliance • Developed SOPs and benchmarks to streamline output • Trained new annotators and reduced rework rates.

2022 - 2023

Quality Analyst (Data Annotation Projects)

CVAT3D SensorObject Detection

Directed quality assurance for LiDAR and 2D/3D point cloud annotation projects. Implemented skill development initiatives, automated validation checks, and feedback systems to ensure annotation scalability and data integrity. Resolved complex sensor fusion edge cases for robust dataset output. • Led QA for LiDAR, 2D, and 3D annotations • Created annotator feedback and skill sessions • Established consistency via automated validation • Improved data integrity through root-cause analysis.

Directed quality assurance for LiDAR and 2D/3D point cloud annotation projects. Implemented skill development initiatives, automated validation checks, and feedback systems to ensure annotation scalability and data integrity. Resolved complex sensor fusion edge cases for robust dataset output. • Led QA for LiDAR, 2D, and 3D annotations • Created annotator feedback and skill sessions • Established consistency via automated validation • Improved data integrity through root-cause analysis.

2021 - 2022

3D LiDAR Annotation for Railway Object Classification

Labelbox3D SensorObject Detection

Executed classification of 3D LiDAR datasets to enable automated railway object detection. Specialized in identifying types such as vegetation, wires, rail lines, and signage for improved railway safety and efficiency. Integrated LiDAR and camera data to enhance AI system performance in edge-case scenarios. • Classified multiple railway-related object types via 3D LiDAR • Mapped objects with 2D camera sensor fusion • Enhanced detection performance by 22% • Executed dataset management with geospatial tagging.

Executed classification of 3D LiDAR datasets to enable automated railway object detection. Specialized in identifying types such as vegetation, wires, rail lines, and signage for improved railway safety and efficiency. Integrated LiDAR and camera data to enhance AI system performance in edge-case scenarios. • Classified multiple railway-related object types via 3D LiDAR • Mapped objects with 2D camera sensor fusion • Enhanced detection performance by 22% • Executed dataset management with geospatial tagging.

2021 - 2021

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Education

Work History