Vetted Research Assistants in California

“Software engineer with experience at Google and the City and County of San Francisco building production AI systems, including a RAG-based internal support chatbot and ML-driven ticket priority tagging. Has scaled data/ML platforms with Airflow on GCP (1M+ records/day, 99.9% SLA) and deployed multi-component systems with Docker and Kubernetes (GKE), using modern LLM tooling (LangChain/CrewAI, Claude/OpenAI, Pinecone/ChromaDB, Bedrock/Ollama).”

“Backend/ML-systems engineer with experience (including Amazon) building real-time face recognition services using PyTorch (MTCNN/FaceNet) and AWS (SQS/S3/Lambda/EC2) with a focus on low latency, burst handling, and cost control. Also led a revenue-critical legacy pricing workflow migration to a serverless event-driven architecture using strangler-pattern rollout, simulation-based validation, and strong security practices (JWT/RBAC/RLS).”

“Backend engineer who built and evolved an internal multi-agent AI research platform (Electron + FastAPI) integrating OpenAI, focused on fast, reproducible experimentation with strong observability and run metadata for debugging. Has led incremental backend refactors with feature flags and parallel validation, and brings production-grade access control expertise from ServiceNow (table/field ACLs and row-level-style enforcement).”

Chancellor Day

Screened

Intern Robotics/Software Engineer specializing in autonomy, computer vision, and controls

San Diego, CA0y exp

“Robotics software engineer with a master’s focused in the field who has integrated a multi-sensor robotics fusion laser system (fault detection, PLC comms, PyTorch-based CV diagnostics, and an engineer-facing status front end) under NDA. Has ROS experience from the University of Michigan Autonomous Robotic Vehicle team using Nav2/SLAM Toolbox/Gmapping with RViz and ROS bag-driven debugging, plus Gazebo simulation work and upcoming drone path-planning optimization research.”

“Robotics software engineer/researcher at Stanford PDML Lab building VisualFT, a ROS2-based visual-tactile sensing system for compliant force-control guidance in acupressure/ultrasound-style manipulation. Also interned at Neocis (dental robotics) improving safety-critical collision detection using Bullet Physics with automated validation and CI (Jenkins/CDash).”

Akashleena Sarkar

Screened

Junior Software Engineer specializing in robotics and real-time distributed systems

Mountain View, CA2y exp

“Robotics software engineer focused on low-compute navigation/SLAM: built a 6-DOF SLAM validation pipeline (IMU + 2D LiDAR + ultrasonic) producing ~1cm OctoMap accuracy and deployed it on an Intel Atom by optimizing particle-filter SLAM with a greedy max-likelihood update. Deep ROS 2 experience (executors, composable/lifecycle nodes, QoS, timestamping) plus simulation and deployment tooling (Gazebo C++ plugins, Docker, CI/CD, ROS 2 build farm) and drone navigation work with MAVROS/PX4.”

“Robotics software engineer who built the full control stack for a fleet of manufacturing/repair robots in Relativity Space R&D (perception, planning, motion control, integration, deployment). Has ROS/ROS 2 experience spanning custom SLAM (LiDAR+IMU), multi-robot coordination, and multi-drone control (Pixhawk 4, minimum-snap trajectories), with strong real-world debugging and simulation/CI testing practices (Gazebo, CI/CD, some Docker).”

Ho-Wei Lu

Screened

Junior Robotics Engineer specializing in motion planning and control

Berkeley, USA1y exp

“Robotics software engineer who built a ROS2-based ping-pong ball interception system on a 7-DOF Sawyer arm, spanning real-time vision, trajectory prediction, and an MPC joint-velocity controller to hit a flying ball within ~1 second. Demonstrated strong real-time debugging and systems integration skills (timestamp-based latency analysis, event-based redesign, ROS2 QoS tuning) and is currently working with Isaac Sim in Docker with GitHub-based CI/CD for assembly-task simulation.”

“Backend/ML systems engineer with experience at SDSC, UCSD, and Media.net, building production semantic dataset/model discovery using embeddings + Solr KNN and LLM-based intent/reranking at 5M+ dataset scale. Emphasizes offline/online separation for predictable serving, has delivered measurable gains (23% retrieval accuracy, 38% latency reduction) and helped secure a $3M+ NSF grant.”