Vetted MATLAB Professionals

“AI/LLM engineer who built a production RAG-based Text2SQL engine using Qdrant, including creating the underlying business/DB documentation, generating a test dataset, and designing detailed SQL-quality metrics for validation. Also partnered with non-technical stakeholders on a speech recognition project to prioritize medical terminology, improving accuracy through targeted corpora, lookup-table correction, and fine-tuning with a modified loss function.”

“AI/ML engineer who led production deployment of a multimodal (text/video/image) RAG system on GCP using Gemini 2.5 + Vertex AI Vector Search, scaling to 10M+ documents with sub-second latency and +40% retrieval accuracy. Strong MLOps/orchestration background (Kubernetes, CI/CD, Airflow, MLflow) with proven impact on reliability (75% fewer incidents) and deployment speed (92% faster), plus experience delivering explainable ML (XGBoost + SHAP + Tableau) to non-technical retail stakeholders.”

“Hands-on builder with projects spanning IoT/backend data systems and robotics: created a flow meter pipeline that sends readings over WiFi to a database and web server with real-time averaging and easy data download. Also diagnosed a hard-to-catch AGV/AMR reliability issue during long autonomous runs (battery voltage drop causing sensor failures) and mitigated it with voltage monitoring and a buffer circuit; familiar with Python/FastAPI API best practices (Pydantic, async, OAuth2/JWT).”

“Robotics software engineer with hands-on depth across simulation (Isaac Sim, Gazebo, Webots), ROS/ROS2 integration, and real-time embedded control. Led an end-to-end quadruped (12-motor) Isaac Sim build from Fusion 360 CAD-to-URDF through physics tuning to achieve a stable walking gait, and optimized a 5-servo arm by cutting IK compute time by 60%+ using lookup tables to eliminate jitter.”

“Recent UC Riverside master’s graduate focused on uncertainty-aware imitation learning for indoor robot navigation, building a full ROS 2 Humble stack (perception, learned policy, uncertainty estimation) with adaptive speed control. Demonstrated strong real-time robotics debugging and systems skills, achieving 92% autonomous navigation success across 100 trials and improving reliability through uncertainty calibration and SLAM/loop-closure optimization.”

“Customer-facing embedded/IoT engineer with experience taking lab prototypes into production, including an IoT medical facility integration where they re-architected firmware with RTOS scheduling, added health monitoring/fault recovery, and implemented secure OTA updates. Strong at real-time diagnosis using end-to-end observability and at tailoring technical demos for both embedded and cloud developer audiences (MQTT/device shadows), helping drive customer confidence and broader deployments.”

“Clinical data scientist who has built an agentic LLM-powered literature review assistant (with RAG-style storage/retrieval) to identify predictors for downstream predictive modeling. Also delivered a patient-focused progression analysis model using Databricks + Airflow orchestration, partnering closely with clinicians to define targets and validate that model insights aligned with clinical expectations.”

“Robotics software engineer who led system design for an Autonomous Trash Collecting ASV presented at the IEEE ICRA 2025 “Robots in the Wild” workshop, integrating YOLOv8-based perception with ROS autonomy logic to detour for trash while preserving a scientific survey mission. Also built ROS2 UAV capabilities combining ArUco detection, RTAB-Map SLAM, and PX4 integration, with strong simulation (Gazebo/VTD/MSC Adams) and CI/CD QA automation experience.”

“ML/NLP engineer with hands-on experience building production systems for unstructured insurance claims and customer data linking. Delivered measurable impact at scale (millions of documents), combining transformer-based NLP, vector search (FAISS/Pinecone), and human-in-the-loop validation, and has strong production workflow/observability practices (Airflow, AWS Batch, Grafana/Prometheus).”

“Robotics software engineer at Earthwise building a full ROS2 Humble warehouse AMR stack for bin picking—owning perception (Livox/Orbbec/RPLidar fusion + calibration), Nav2 navigation with custom planners/behavior trees, and application-layer nodes (barcode scanning, safety monitoring, web HMI). Demonstrated strong real-world debugging and performance tuning (sub-cm AprilTag docking; ~80% reduction in localization failures) plus solid simulation/CI practices (Gazebo + Docker + GitHub Actions).”

“Hands-on AI/LLM practitioner who built a RAG-based customer support chatbot and tackled production issues like data chunking complexity and response-time lag. Uses techniques such as overlapping chunks, semantic search, context engineering, and query routing, and has experience presenting technical demos/workshops to developer audiences.”

“Robotics software/vision engineer with hands-on experience building motion-tracking systems that fuse camera-based 3D tracking with IMU orientation to reproduce tool motion for automated spray painting. Has implemented ROS nodes/packages for Orbbec camera streaming and SAM3-based segmentation, plus CAN bus coordination between robots and Dockerized deployment for a pick-and-place robotic cell.”

“Robotics/ML candidate building an individual pedestrian trajectory forecasting system by adapting a GAN-style Social-GN training architecture from LSTM to a transformer-based AgentFormer design. Also has hands-on embedded robotics experience debugging lane-following behavior on a JetBot by tuning PID control, and uses Docker for reproducible training environments.”

“Founding engineer at a previous company who reports owning all products end-to-end from ideation through launch and inventing the company's flagship technology/products; emphasizes making development tradeoffs to deliver safe, functional, usable, ergonomic products.”

“Built and deployed LLM-powered agentic chatbot and text-to-SQL systems using LangGraph/LangChain (and Bedrock), structuring workflows as DAGs with planning/replanning and validation to improve tool-calling reliability and reduce hallucinations. Operates production feedback loops with online/offline metrics, drift detection, and LangSmith-based evaluation pipelines, and regularly partners with business stakeholders and clinicians using slide decks and visual charts.”

“Robotics software architect who built an automated pick-and-place palletizing prototype at BLACK-I-ROBOTICS, spanning perception (multi-RealSense fusion, segmentation, 6D pose, ICP), GPU-accelerated motion planning (MoveIt 2 + NVIDIA CuRobo), grasp generation, and safety (human detection + safe mode). Also brings cloud/CI/CD depth from VERIDIX AI (AWS Cognito/Lambda/ECS and CodePipeline stack) and demonstrated strong debugging chops by reducing outdoor rover EKF drift to ~5 cm via Allan variance-based IMU tuning.”

“Robotics/Perception Software Engineer at Berkshire Grey who built and hardened a production ROS-based perception + supervision stack for autonomous trailer-unloading robots (RGB-D + LiDAR), including grasp/geometry estimation and segmentation. Diagnosed real-time behavior issues by instrumenting ROS pipelines, then implemented runtime RANSAC-based compensation for LiDAR yaw bias and TF-window validation; also supports containerized deployment on Kubernetes and is actively porting the system from ROS1 to ROS2.”

“Robotics software engineer who built a full LiDAR SLAM pipeline from scratch in C++ (ICP, pose graph optimization, loop closures) and validated it quantitatively against ground-truth datasets. Extensive ROS2 experience from academics and an internship building a localization system, plus practical deployment work using Docker across x64 and ARM edge devices; also trained RL policies for TurtleBots in Gazebo.”

“Robotics software engineer with hands-on ROS2 experience building perception-driven navigation for AMRs, integrating YOLO11 + Depth Anything V2 and multi-sensor fusion (LiDAR/RGB-D/IMU) to boost pose accuracy by 30%. Strong in real-time debugging and edge deployment on NVIDIA Jetson (ONNX/CUDA), plus cloud-enabled telemetry (Azure) and simulation-driven testing (Isaac Sim) that cut physical test cycles by 25%.”

“Robotics software candidate whose thesis focused on multi-robot warehouse coordination using MAPPO reinforcement learning, trained in simulation (LBF environment, Isaac Sim/RViz) and deployed onto three real-time robots. Built custom ROS 2 Humble nodes for multi-robot control with namespaces, TF broadcasting, and an RL pipeline integrating LiDAR odometry and camera observations.”

“Robotics software engineer with hands-on experience building Visual-Inertial SLAM and ROS2 sensor-fusion pipelines for autonomous warehouse forklifts (ArcBest), including rigorous calibration (AprilTags, Allan variance, temporal sync) and recovery features like pose injection. Also implemented RL-based local planning at RollNDrive using Isaac Sim with domain randomization to bridge sim-to-real, improving real-world navigation success back to ~90% after initial deployment.”

“Built a production LLM-RAG system at McKesson to let internal healthcare operations teams query large volumes of unstructured operational documents via natural language with source-backed answers, designed with HIPAA/FHIR compliance in mind. Demonstrated strong production engineering across hallucination mitigation, retrieval quality tuning, and latency/scalability optimization, using LangChain/LangGraph and Airflow plus rigorous evaluation/monitoring practices.”

“Product-focused full-stack engineer (Spring Boot/Django + React/TypeScript) with deep experience building multi-tenant, enterprise workflow and supply-chain/order-tracking systems. Owned an end-to-end Workflow SLA Breach Prediction & Alerting feature integrating Azure ML for a cloud workflow platform used by ~10,000 enterprise users, and has hands-on AWS operations experience resolving real production latency/scaling incidents via query optimization and Redis caching.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”