KGeN - 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 :

- 1 to 4 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 23 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