Research Analyst – Egocentric AI & Robotics Data Intelligence

Research Analyst – Egocentric AI & Robotics Data Intelligence

About the Role

At Humyn Labs, we are building egocentric video datasets from real-world environments — residential, agricultural, manufacturing, and construction — to train the next generation of robotic AI models.

We believe human-collected, real-world data fundamentally outperforms synthetic or simulation-based data for robotic training. Your job is to prove it.

We are looking for a Research Analyst who can rigorously compare Human Labs datasets against sim/synthetic alternatives, publish compelling research that demonstrates the superiority of real human-collected egocentric data, and help position Human Labs as the definitive source of ground-truth robotics training data.

What You'll Own

Robotics Model Performance Research

• Evaluate how robotic models trained on Human Labs egocentric data perform vs. models trained on synthetic or simulation data
• Benchmark across real-world domains:
• Residential (household tasks, navigation, object interaction)
• Agricultural (field operations, crop handling, terrain variability)
• Manufacturing (assembly, quality inspection, tool use)
• Construction (site navigation, material handling, safety scenarios)
• Track metrics such as task success rate, generalization, robustness, and sim-to-real transfer gap
• Continuously publish performance comparisons that highlight real-world data advantages

Egocentric Video Dataset Analysis

• Deep-dive into Human Labs egocentric video datasets — understand what makes them uniquely valuable
• Analyze dataset characteristics including:
• First-person perspective richness and scene diversity
• Labeling precision, bounding quality, and annotation consistency
• Temporal depth and action continuity
• Environmental variability (lighting, motion, noise, terrain)
• Compare against publicly available sim datasets (e.g., AI2-THOR, Habitat, Isaac Sim, CARLA) and synthetic alternatives
• Identify and articulate what differentiates Human Labs data quality from other vendors

Labeling & Annotation Quality Intelligence

• Develop a structured framework to evaluate and score dataset annotation quality
• Focus on what matters for robotics training:
• Bounding box precision and consistency
• Action and event labeling accuracy
• Depth, pose, and spatial annotation quality
• Edge case coverage in real-world conditions
• Showcase how Human Labs labeling standards outperform industry benchmarks

Research Publishing & Thought Leadership

• Publish research reports, white papers, and blog posts that:
• Demonstrate human data superiority over sim/synthetic for robotic training
• Highlight performance gaps when models trained on sim data are deployed in the real world
• Position Human Labs as a pioneer in real-world egocentric robotics data
• Stay deeply read on:
• Robotics learning research (imitation learning, behavior cloning, reinforcement learning from demonstrations)
• Egocentric video understanding and first-person AI
• Sim-to-real transfer literature
• Competing dataset vendors and benchmark ecosystems

What We're Looking For

• 2–5 years of experience in ML research, robotics data, computer vision, or applied AI
• Strong understanding of robotics training pipelines and data requirements
• Familiarity with egocentric or first-person video datasets (e.g., Ego4D, EPIC-Kitchens, or similar)
• Knowledge of sim/synthetic data platforms (Isaac Sim, AI2-THOR, Habitat, CARLA, or similar)
• Experience with dataset evaluation, annotation quality assessment, or benchmarking
• Ability to write clear, publishable research for both technical and non-technical audiences
• Genuine curiosity about the real-world vs. synthetic data debate in AI

Technical Skills

• Python (mandatory)
• PyTorch or TensorFlow
• Video processing tools (OpenCV, FFmpeg, or similar)
• Familiarity with:
• Robotics learning frameworks (ROS, LeRobot, or similar)
• Annotation and labeling tools (CVAT, Scale AI, Labelbox, or similar)
• Evaluation metrics for robotics and video understanding
• Experience reading and synthesizing ML research papers
• Bonus: hands-on experience with sim environments or robotic datasets

Ideal Mindset

• Deeply read on robotics AI, egocentric video, and dataset research
• Analytical and detail-oriented — able to spot what makes one dataset better than another
• Passionate about real-world data and its role in making robots actually work
• A strong communicator who can turn data comparisons into compelling research narratives
• Excited to build Human Labs reputation as the gold standard in robotics training data

What Success Looks Like in 90 Days

• First research report published comparing Human Labs egocentric data vs. sim/synthetic alternatives on at least one robotic domain
• Benchmarking framework live across 2–3 robotics or video models
• Dataset quality scoring system operational with clear differentiation metrics
• At least 2 domain-specific analyses (e.g., residential vs. agricultural) highlighting real-world data advantages