Vetted Algorithms Professionals

“At Esri, the candidate is building a production LLM-powered WebGIS AI framework that embeds an AI assistant into web maps and routes natural-language requests into ArcGIS JavaScript SDK functions via a LangGraph-orchestrated, multi-agent system. They emphasize production reliability and scale (strict tool calling/JSON, live schema validation, query guardrails) and rigorous evaluation/observability using LangSmith, offline prompt datasets, and latency/tool-call accuracy tracking.”

“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.”

“AI engineer building and deploying deep-learning-based optimization/control systems for petrochemical plants, with a focus on maintaining operational stability under real-world constraints. Core contributor to model and inference design; introduced a stability-focused non-linear objective and sped up second-layer optimization via on-the-fly first-order approximations. Experienced using Kubernetes for end-to-end testing and effective in translating customer expectations into measurable evaluation plots for non-technical stakeholders.”

“ML/LLM practitioner with experience at Truveta building an LLM-based evaluation framework; identified non-overlapping evaluator failure modes and proposed an ensemble approach that enabled scaling training data and drove ~5% performance gains across multiple internal projects. Strong focus on robustness to distribution shift (augmentation/domain adaptation/meta-learning) and production reliability via monitoring, drift detection, and safe fallbacks.”

“Applied ML/LLM practitioner working in healthcare-facing products, using RAG and LoRA fine-tuning on medical data and implementing production monitoring (confidence scoring) for clinician oversight. Has hands-on experience debugging agentic/LLM pipelines (including OCR preprocessing fixes) and regularly delivers technical demos to doctors, investors, and conferences—contributing to adoption and even helping close a funding round through end-to-end pipeline walkthroughs.”

“Software engineering candidate who built a compiler-like Python tool to translate between Python code and UML-style diagrams (and back). Also has hands-on AWS experience building a distributed pub/sub system using services like Lambda, API Gateway, ELB, WAF, VPC, and DynamoDB, plus ML projects using Kaggle datasets (e.g., diabetes risk analysis).”

“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.”

“Built and owned an end-to-end AV operations automation and dashboarding platform for USC event operations, used daily to coordinate hundreds of live events. Delivered a React/TypeScript full-stack system integrating Smartsheet APIs with strong reliability practices (typed contracts, validation/fallbacks, safe rollouts) and experience with queue-based microservice patterns (idempotency, retries, DLQs, monitoring).”

“Senior Engineering Manager at Roche leading two Scrum teams building internally shared (“inner-sourced”) tools and libraries for a healthcare enterprise. Has led security/compliance-first architecture decisions (e.g., Python AI modules running inside a Java container) and front-end modularization (Angular monorepo to module federation), with a strong focus on developer experience via automated Swagger/OpenAPI documentation and robust testing/versioning practices.”

“AI/ML practitioner with experience spanning audio/LLM applications (built "Iota" using Whisper, tiktoken, and a local Ollama-served LLM) and healthcare ML (Facemed.ai; UChicago Radiology). Demonstrates a production-oriented mindset—focus on data/model fit, deterministic field testing, and operational safeguards—and has improved research evaluation workflows via a hash-table-based concurrent model tracking approach.”