Vetted PyTorch Professionals

“Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.”

“Robotics/IoT engineer who deployed a fog-enabled real-time monitoring system (edge Raspberry Pi + MQTT + cloud logging) and validated it via an IEEE-indexed publication. Strong in autonomous navigation with ROS/Gazebo, SLAM/localization, and cross-layer debugging using timing/transform-delay correlation. Extends Python computer vision pipelines (YOLO + OpenCV/Albumentations) for custom datasets and weather-specific conditions.”

“Embedded/real-time robotics-style engineer with hands-on STM32 development, sensor integration, and low-level drivers, focused on deterministic control behavior. Demonstrated systematic debugging of jitter/latency by instrumenting the sensing-to-actuation pipeline and eliminating blocking via interrupts, hardware timers, and DMA; also designs asynchronous, message-based interfaces for distributed real-time components. Familiar with ROS/ROS2 concepts (nodes/topics/callbacks) though not yet deployed a full production ROS system.”

“Robotics/ML engineer who has built SO(3)-equivariant models for robotic manipulation, including custom equivariant layers and differentiable point-cloud rasterization/derasterization workflows. Also brings 2 years of DevOps experience in banking systems, automating CI/CD and infrastructure at scale (managed 180 OCI servers; reduced rebuild downtime by 80%).”

“Software engineer with experience delivering customer-facing solutions across consulting and engineering contexts (Deloitte, Coherent), including a finance reconciliation system and a firmware validation tool integrated into existing test infrastructure. Demonstrates strong on-site/customer collaboration, rapid iteration, and high-pressure debugging (CARLA demo fix), with measurable impact and a focus on adoption through familiar workflows and clear documentation.”

“Robotics/AV-focused engineer who built an end-to-end gesture controller for a GEM e2 autonomous vehicle using YOLOv8 pose and ROS, including model training, ROS perception nodes, and a safety-oriented state machine (stop override + hold-to-register). Also has internship experience at Intramotev integrating LiDAR object detection via Redis pub/sub and performing sensor-frame calibration (roll/pitch correction using ground-plane normals), plus Dockerized deployments and Gazebo-based testing.”