Vetted Research Assistants

“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”

“Robotics-focused engineer with ROS 2 experience who has built and debugged real-time, distributed control/orchestration systems under production-like latency and safety constraints. Led platform changes at Persona for a real-time verification orchestration system using deterministic state machines and async workers, and has hands-on experience stabilizing multi-robot navigation/SLAM behavior using rosbag, RViz, and stress testing in simulation (Gazebo).”

“Python backend engineer focused on high-throughput document/PDF processing systems, building end-to-end pipelines that extract structured content for downstream NLP use cases. Demonstrates strong practical MLOps-adjacent infrastructure skills: Kubernetes deployments, GitLab CI, GitOps workflows, and an incremental migration to AWS using EC2/Lambda tradeoffs. Deep hands-on optimization experience (selective OCR, layout-aware extraction, parallelism, caching, idempotency, and backpressure/autoscaling).”

“Robotics software engineer (master’s student) who placed 3rd in the CMU VLA challenge and presented at IROS, building an LLM-powered language system (Gemini 2.5) for mobile-robot scene Q&A and language-based navigation. Hands-on ROS1/ROS2 experience including ros2_control + PILZ planning for a KUKA arm, plus simulation (Gazebo) and containerized submissions with Docker.”

“Robotics architect intern/new-grad focused on warehouse AMRs, building ROS2 sensor-fusion and SLAM stacks (FastSLAM-style particle filter) and validating in Gazebo with ground-truth metrics. Also interned at ASML debugging real-time in-vacuum robot behavior via Python state-machine telemetry scripts, identifying a firmware driver issue impacting throughput.”

“Robotics/embedded builder who trained an aviation-specific LLM and deployed it offline on an NVIDIA Jetson for an in-flight voice assistant, solving performance and cabling constraints with NVMe storage and Bluetooth. Also has hands-on Raspberry Pi/Arduino robot builds (including a cigarette-butt picking prototype with hydraulic actuation) plus Docker-based FEA work using FEniCS/Gmsh and strong CI/CD + automated testing practices.”