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Vetted Graduate Research Assistants in the NYC Metro
Pre-screened and vetted in the NYC Metro.
Nipun Agarwal
Junior Software Development Engineer specializing in AI/ML systems
Dana Spiegel
Executive CTO and product leader specializing in enterprise AI/ML and digital transformation
Devdatt Golwala
Mid-level Data Scientist/ML Engineer specializing in LLMs, NLP, and recommender systems
Barath Kumar Jayachandran Kanchanamalini
Junior Robotics & Controls Engineer specializing in UAV autonomy and embedded systems
“Robotics software engineer focused on autonomous drones and mobile robotics: implemented a sliding mode inner-loop controller and a RealSense T265 VIO state-estimation pipeline integrated into ArduPilot EKF3 for GPS-denied indoor flight. Strong simulation-to-deployment experience (Gazebo/MAVROS to firmware), ROS2 networking/debugging, and hands-on validation through multi-sensor trials and log analysis.”
Rishi Kanade
Mid-level Data Scientist specializing in machine learning, computer vision, and generative AI
Ting-You Chung
Entry-level Robotics Engineer specializing in ROS2 autonomy and motion planning
“Robotics software engineer who led an energy-aware persistent monitoring project on TurtleBot4/ROS2, building the full stack from simulation and motion control to an energy consumption model and control algorithm implementation. Developed custom ROS2 Python nodes (battery + cmd_vel logging), integrated with Nav2, and handled multi-robot coordination via DDS while troubleshooting network/QoS issues. Also built and tuned a human-aware navigation behavior using Gazebo-based testing and data-driven threshold optimization.”
Jainam Manish Jain
Junior Robotics Software Engineer specializing in autonomous systems and sensor fusion
Jeet Choksi
Mid-level Machine Learning Engineer specializing in real-time AI and data platforms
“ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.”