Austin J.

Key Skills

Software

Encord

Other

Top Subject Matter

Humanoid robotics video

vision-language-action (VLA) model training

robot perception and motion interaction

Top Data Types

Video

Text

Image

Top Task Types

Object Detection

Segmentation

RLHF

Freelancer Overview

VLA Video Annotation Specialist - Level B (Remote). Core strengths include Encord and Other. Education includes Bachelor of Science, Florida International University (2016). AI-training focus includes data types such as Video and Text and labeling workflows including Object Detection, Segmentation, and RLHF.

Labeling Experience

VLA Video Annotation Specialist - Level B (Remote)

EncordVideoObject Detection

You annotated humanoid robotics video frame-by-frame as part of a VLA (vision-language-action) model training project. You drew bounding boxes and applied object detection labels across sequential video frames while keeping placement and class assignment consistent. You followed detailed project schemas and performed QA-ready guideline-driven work to support robot perception training. • Bounding boxes/object detection labels across consecutive frames • Segmentation masks for persons, objects, and environmental elements • Point keypoints/pose landmarks for body joints across frames • Classification attributes and scene-level tags per frame/clip

2024 - Present

Video & Image Data Labeling Specialist (Freelance)

OtherVideoSegmentation

You labeled large-scale image and video datasets for computer vision model training across detection, action recognition, scene classification, and semantic segmentation tasks. You created and refined bounding boxes for tracking across multi-frame sequences, supporting downstream autonomous and surveillance AI. You applied pixel-level segmentation and human body keypoints/joint positions to support perception, biomechanics, and activity recognition. • Bounding boxes for object tracking across multi-frame video sequences • Pixel-level segmentation for persons, vehicles, and objects • Human body keypoints and joint positions across motion video datasets • QA review and self-QA with inter-annotator agreement focus

2022 - 2024

Text & Multimodal Data Annotation Contributor (Remote)

OtherTextRLHF

You contributed to text annotation and NLP labeling projects for large language model training, including named entity recognition, sentiment classification, intent labeling, and RLHF preference ranking. You evaluated model-generated outputs for accuracy, coherence, safety, and helpfulness, producing structured written feedback for LLM fine-tuning. You also labeled multimodal image-text pairs using consistent category schemas for vision-language datasets. • Named entity recognition (NER), sentiment, and intent labeling • RLHF preference ranking and related preference judgments • Image-text pair annotation for multimodal vision-language datasets • Annotation logs, onboarding/qualification, and QA escalation for ambiguous cases

2021 - 2022

Education

Work History