Vetted Research Assistants in the DMV

“Internship project at Builder Market: built an end-to-end production multimodal LLM application that estimates renovation/replacement costs from appliance photos (CLIP embeddings) or text descriptions, combining fine-tuning with agentic RAG. Focused heavily on real-world performance constraints—latency and cost—using parallel agent workflows, model routing to smaller/open-source models, re-ranking, and retrieval chunking, and collaborated closely with CEO/co-founders to deliver the solution.”

“Robotics/AV perception engineer who built a semantic-segmentation road detection system and integrated it into a ROS-based real-time pipeline (ROS bag camera feed to live monitor) achieving ~12 FPS. Strong in practical deployment work: solved multi-library versioning issues (ROS/OpenCV/TensorFlow), containerized the stack with Docker, and optimized inference by shifting runtime to C++ for large latency gains on NVIDIA hardware.”

“Robotics software engineer with hands-on ROS (ROS 1) experience building sensor-processing and state-based control pipelines in Python/C++. Demonstrated measurable reliability and performance gains in autonomous navigation—cut runtime failures by 30%, reduced replanning by 35%, and improved debugging efficiency by 40%—using timing-aware state machines, message/interface discipline, and simulation/testing with Gazebo, rosbag, Docker, and CI/CD.”

“Robotics software engineer with experience spanning safety-critical embedded medical hardware (low-cost neonatal baby warmer with PID temperature regulation) and advanced multi-robot planning research (belief-space planning with abstraction + MCTS to handle uncertainty). Strong ROS/ROS2 practitioner (Nav2/SLAM Toolbox/MoveIt) who builds custom packages (e.g., Insta360 panoramic imaging) and is hands-on debugging real robots from SLAM/frontier exploration to multi-robot collision avoidance and real-time performance.”

“AI engineer who built an agentic, production-deployed LLM workflow for tobacco violation parsing and automated multi-case creation, using six specialized agents and a human-in-the-loop confidence-threshold routing design. Addressed data privacy constraints by generating synthetic datasets with LLM prompting, and orchestrated reproducible end-to-end pipelines in LangChain with robust testing and evaluation (precision/recall, micro-F1).”