Vetted Research Assistants in California

“Built and productionized a dual-agent LLM invoice-processing system for GFI Partners, adding guardrails and audit trails to earn stakeholder trust and drive adoption while cutting operational burden by 75%. Uses LangSmith observability to diagnose real-time workflow regressions and has experience teaching agentic AI concepts (e.g., at Carnegie Mellon) through hands-on, scaffolded demos.”

“Growth-creative marketer with hands-on paid social and short-form video experience across restaurant and beauty (Tom Ford Beauty). Runs structured creative experiments (Meta A/B ad sets, Meta Pixel conversion tracking) and adapts strategy to platform behaviors (TikTok comments vs IG sharing). Has led creators/editors and drove a reported 100% ROAS lift on a Valentine’s Day TikTok ads iteration.”

“Built and deployed an LLM-powered RAG system (PubChemRAG) for a 4D Mitospace project to compare mechanisms across a ~100-drug glossary and surface expected pathway/phenotypic differences in mitochondrial imaging. Worked closely with biochemistry and microscopy experts to design tiered evaluation benchmarks, iterating on prompts, retrieval quality (corpus hygiene, chunking strategy), and model outputs under GPU constraints using LangChain.”

“Built and owned an end-to-end VR surfing simulator in Unity/C#, integrating Arduino inputs, external tracking, and a motion platform with multisensory feedback. Deep focus on VR comfort and control stability (PD control, quaternion-based orientation, latency/jitter mitigation) validated via IMU measurements and 200+ user studies, plus some Unreal gameplay framework and replication/prediction experience.”

“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).”

“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).”