AI Researcher

Role Summary

As an AI Researcher, you will focus on exploring, designing, and developing advanced AI/ML algorithms to address complex challenges in robotics and industrial applications. This role involves conducting fundamental research, prototyping innovative solutions, and collaborating with engineering teams to integrate research outcomes into practical applications. You will work at the forefront of AI innovation, contributing to autonomy, perception, and decision-making in intelligent robotic systems.

Key Responsibilities

1. Research & Development in AI

• Conduct Fundamental Research: Investigate and develop new AI/ML methodologies, including reinforcement learning, generative AI, computer vision, and multi-agent systems.
• Solve Complex Robotics & AI Challenges: Address challenges in robotic perception, control, planning, and decision-making using innovative AI techniques.
• Prototype Novel AI Solutions: Develop proof-of-concept models and validate cutting-edge algorithms for integration into production systems.

2. Explore Emerging AI Technologies

• Stay at the Forefront of AI Innovation: Research the latest advancements in AI, ML, and robotics, assessing their applicability to industrial automation.
• Experiment with Generative AI: Develop generative AI models for dynamic path planning, sensor simulation, and synthetic data generation.
• Advance Reinforcement Learning: Innovate in reinforcement learning (RL) techniques to improve robotic task execution and adaptive decision-making.

3. Data-Driven Innovation

• Work with Large-Scale Datasets: Process and analyze IoT, sensor, and robotic data to develop data-driven AI solutions.
• Generate & Leverage Synthetic Data: Develop methodologies for synthetic data generation to train AI models in data-scarce environments.
• Optimize Data Processing Pipelines: Research efficient data preprocessing and augmentation techniques to improve model generalization.

4. Model Validation & Optimization

• Validate AI Research Outcomes: Conduct rigorous testing and benchmarking to assess AI models real-world performance and scalability.
• Optimize for Edge AI & Embedded Systems: Ensure AI models can run efficiently on low-power embedded devices and edge computing platforms.
• Iterate & Improve Models: Refine AI models based on experimental results, deployment feedback, and real-world data constraints.

5. Collaboration & Knowledge Sharing

• Work Across Research & Engineering Teams: Collaborate with AI/ML engineers, robotics teams, and Edge AI experts to transition research into production.
• Mentor & Guide AI Teams: Share expertise with team members and provide technical leadership in AI/ML research initiatives.
• Publish & Present Findings: Document research in scientific journals, conferences, and internal reports, representing the company at industry events.

6. Innovation & Intellectual Property

• Develop Patentable AI Innovations: Identify opportunities for intellectual property generation, leading patent development for novel AI technologies.
• Contribute to Open-Source AI Projects: Engage in open-source AI and robotics initiatives, enhancing industry collaboration.

7. Ethical & Responsible AI

• Ensure Ethical AI Practices: Develop AI models aligned with ethical AI principles, ensuring transparency, fairness, and accountability.
• Address Bias & AI Robustness: Implement strategies to detect and mitigate biases in AI models, ensuring reliable performance across diverse scenarios.

Required Qualifications - Education & Experience

• Ph.D. in Computer Science, Machine Learning, Robotics, or a related field.
• Exceptional candidates with a Masters degree and significant research experience will also be considered.
• 3+ years of experience in AI research, focusing on machine learning, deep learning, or robotics applications.
• Proven track record in publishing AI/ML research in peer-reviewed journals and top conferences (e.g., NeurIPS, ICRA, CVPR, ICML).

Technical Skills

• Machine Learning & AI Research:
• Proficiency in TensorFlow, PyTorch, Scikit-Learn, and algorithm design for supervised, unsupervised, and reinforcement learning.
• Strong background in deep learning architectures (CNNs, RNNs, Transformers, GANs, and GNNs).
• Programming & Development:
• Advanced proficiency in Python, with experience in C++ or Java preferred for robotics applications.
• Familiarity with parallel computing, optimization libraries, and high-performance AI frameworks.
• Robotics & Perception:
• Knowledge of robotics software stacks (ROS, Gazebo, OpenCV, PCL) and integration with AI/ML models.
• Experience in SLAM, computer vision, 3D reconstruction, and sensor fusion techniques.
• Edge & Cloud Computing:
• Experience deploying AI models on edge devices (NVIDIA Jetson, TPUs, NPUs) and utilizing cloud platforms (AWS, Azure, GCP) for AI research.

Preferred Qualifications

• Cross-Disciplinary AI Research: Background in control theory, biomechanics, or physics-based modeling for robotics.
• Industrial Automation & IoT: Familiarity with robotics in industrial automation, IoT ecosystems, and autonomous factory systems.
• Patent & IP Development: Experience leading patent development for AI/ML innovations.
• Open-Source Contributions: Active participation in open-source AI/ML communities or contributions to AI libraries.