Vetted Engineering Professionals in the Bay Area

“Robotics software engineer (10+ years ROS/ROS 2) currently leading the perception stack for Omron’s AMR fleet, including a scalable factory SLAM system that combines vision with laser SLAM to handle corridor aliasing. Strong in real-time embedded optimization on NVIDIA Jetson (CUDA + profiling) and fleet-scale validation via multi-robot Isaac Sim scenarios (USD-to-ROS 2 bridging, Nav2 in crowded scenes). Also contributed to a cloud-native reality-capture/3D reconstruction pipeline at Hilti using Docker and Kubernetes.”

“Backend/full-stack engineer (5+ years) with Shopify experience integrating LLM/RAG workflows into production APIs. Owned a Python TensorFlow Serving inference pipeline connected to Java microservices via gRPC, optimizing tail latency at ~10k concurrent load and improving retrieval relevance with embedding and evaluation work. Strong Kubernetes/EKS + GitOps/CI/CD background, including monolith-to-microservices migrations and event-driven streaming patterns.”

“Solutions engineer at a Voice AI startup building and deploying voice agents for self-storage customers, owning the full lifecycle from cold outreach and demos through contract negotiation, FMS/API integration, and post-sales optimization. Also has Audi/Porsche experience demoing pre-production vehicles and autonomous driving features (L3 lane changes) to internal stakeholders and VW Group leadership, with a strong track record of tailoring technical narratives to varied audiences and improving conversion by adapting agents to regional dialects.”

“Backend/data engineer from ASML who modernized a legacy SAS-based statistical processing system into a cloud-native AWS platform (Lambda/FastAPI, Step Functions/EventBridge, Glue, S3/RDS) with strong reliability and data-quality practices. Demonstrated measurable performance wins (RDS query reduced from 90+ seconds to <5 seconds) and hands-on incident ownership for production ETL pipelines.”

“SRE-focused engineer with experience at Sony Interactive Entertainment productionizing high-throughput LLM/agentic systems on Kubernetes, including GPU-aware autoscaling and warm-pool strategies to manage latency and cost under traffic spikes. Demonstrates strong incident response using Prometheus/Grafana + Jaeger tracing (e.g., resolving recursive agent loops and restoring 99.9% availability within minutes) and partners closely with sales/customer teams through PoV demos and developer workshops.”

“Robotics-focused candidate who built core software for a ROS2 pick-and-place arm that sorted colored cubes using an overhead camera and an AI color-recognition model. They owned arm modeling in ROS, motion logic (coordinate-to-IK-to-joint paths), and created a data-driven linear error-correction calibration to mitigate camera/AI coordinate bias, plus tuned motion speed to improve real-world repeatability.”