Vetted Research Assistants

“ML/NLP engineer focused on large-scale data linking for e-commerce-style catalogs and customer records, combining transformer embeddings (BERT/Sentence-BERT), NER, and FAISS-based vector search. Has delivered measurable lifts (e.g., +30% matching accuracy, Precision@10 62%→84%) and built production-grade, scalable pipelines in Airflow/PySpark with strong data quality and schema-drift handling.”

“Backend/ML engineer who has built both enterprise data pipelines and real-time AI products: modular Python (Flask/FastAPI) services integrating automation scripts and low-latency ML inference (MediaPipe, PyTorch) plus OpenAI-powered feedback. Demonstrated measurable performance wins (~30% faster HR workflows; ~40% faster AWS pipelines across 100+ Oscar Health feeds) and strong multi-tenant/data-isolation patterns (schema-based isolation, RBAC, microservices).”

“Backend/data engineer with hands-on production experience building Python (FastAPI/Flask) data enrichment services secured with Okta OAuth2 and monitored via Splunk/Dynatrace. Has delivered AWS event-driven and data-migration solutions (Lambda + Kafka to EKS; Glue from on-prem Oracle to S3/data lake) and modernized Informatica match/merge logic to cloud services using parallel-run parity validation and stakeholder sign-off.”

“ML engineer with hands-on experience taking a Gaussian Process Regression-based intelligent survey timing system from build to real-world deployment, including a 3-week RCT on 120 participants and measurable improvements (15% response rate, 23% data quality). Also served as a key technical resource at CData for customer-facing demos and debugging hundreds of production issues, bridging engineering with Sales and Customer Success.”

“Robotics engineer who built and scaled a distributed perception stack on a Unitree Go1 quadruped, coordinating 5 Jetson Nanos and a Raspberry Pi to capture, aggregate, and stream multi-camera video in real time via UDP/GStreamer and custom ROS nodes. Also implemented a YOLOv9-based detection pipeline enhanced with Grad-CAM-driven selective image enhancement (e.g., MIRNet/UFormer) to improve real-time detections and robot reactions to visual stimuli.”

“Robotics software engineer with hands-on ROS (ROS 1) experience building sensor-processing and state-based control pipelines in Python/C++. Demonstrated measurable reliability and performance gains in autonomous navigation—cut runtime failures by 30%, reduced replanning by 35%, and improved debugging efficiency by 40%—using timing-aware state machines, message/interface discipline, and simulation/testing with Gazebo, rosbag, Docker, and CI/CD.”

“Built and shipped a production LLM-powered decision system that replaced a slow, inconsistent manual review process by turning messy text into structured, auditable outputs behind an API. Demonstrates strong end-to-end ownership of reliability and operations (schema validation, retries/fallbacks, latency/cost controls, monitoring for drift) and a disciplined approach to evaluation and regression testing. Experienced collaborating with non-technical reviewers to define success criteria and deliver interpretable outputs that get adopted.”

“Robotics software engineer focused on ROS 2 multi-robot coordination, having built task allocation and reliable inter-robot communication for simulated TurtleBot3 fleets. Demonstrates strong integration/debugging skills across Nav2 + gmapping (SLAM drift, TF tree, odometry/sensor fusion) and pairs it with production-minded tooling—Docker/Kubernetes deployments and CI/CD simulation testing via GitHub Actions.”

Jonathan Grebe

Screened

Mid-level Robotics Software Engineer specializing in ROS, C++ and embedded Linux

Ottawa, Canada7y exp

“Robotics software lead at Icor who grew from intern to owning the end-to-end software lifecycle for a mobile manipulator platform deployed to 300+ customers globally. Deep hands-on ROS2/MoveIt2 and navigation-stack integration (URDF/TF, sensors, behavior engine) plus production infrastructure (CI/CD, OTA, field OS upgrades) and real-world performance tuning for motion planning in EOD multi-robot environments.”

Shivaram Srikanth

Screened

Junior Robotics Engineer specializing in computer vision and sensor fusion

Mountain View, CA2y exp

“Robotics software engineer with ~3 years of ROS experience spanning drone autonomy and perception. Recently improved drone barcode scanning by shifting to segmentation and deploying an optimized instance-seg model to edge hardware (FP16 quantization, convex-hull masks), while also building ROS drivers/parameters for field-tunable behavior. Has hands-on experience integrating LegoLOAM and calibration/TF systems, including creating RViz visualization tools to validate transforms and debugging real-world drift issues caused by lighting/glare.”

“BI/analytics candidate with healthcare and product analytics experience spanning Honor Health and ASU. They’ve worked on messy multi-system hospital supply data and also owned analytics for an AI-powered tax assistant, with quantified outcomes including 97% faster search, 92% retrieval accuracy, 30% fewer ad hoc procurement requests, and 15% lower operational cost.”

“ROS2/Python robotics engineer who led a 4-person team building a simulated multi-robot warehouse system (SLAM + NAV2 + centralized task allocation) in Gazebo Ignition, including a distance/priority-based controller that reduced task completion time by ~30%. Also has hands-on real-time debugging/tuning experience for both mobile robots and a MyCobot 600 Pro manipulator, plus simulation work in CARLA using RL (TD3) and Social-LSTM for pedestrian behavior modeling.”

Abhishek Ingle

Screened

Junior Full-Stack & AI Software Engineer specializing in React/Next.js and LLM systems

Bloomington, IN2y exp

“Backend engineer with hands-on experience building low-latency, high-concurrency real-time chat on AWS (Node.js/Socket.IO/MongoDB) and improving reliability under unstable networks, contributing to ~40% user adoption growth. Also built FastAPI-based AI assistant context retrieval (RAG) APIs with embeddings/vector search, and has strong production experience in rate-limit handling, async refactors with safe rollout, and Supabase Auth/RLS optimization.”

Thira Patel

Screened

Junior Robotics/Mechatronics Engineer specializing in SLAM, motion planning, and autonomy

1y exp

“Robotics software engineer focused on autonomy stacks for high-payload AMRs using ROS2/Nav2, with hands-on expertise in SLAM/localization and sensor fusion (RTK GPS, IMU, wheel odom, ZED2) to eliminate drift and stabilize real-time behavior on deployed hardware. Also built multi-robot coordination in ROS2/Gazebo and uses Docker + Git/CI-style testing to create reproducible simulation-to-hardware pipelines.”

prashanth Jamalapurapu

Screened

Mid-level AI/ML Engineer specializing in data engineering, LLM/RAG pipelines, and recommender systems

5y exp

“Research assistant at St. Louis University who built and deployed a production document-intelligence RAG system (Python/TensorFlow, vector DB, FastAPI) on AWS, focusing on grounding to reduce hallucinations and latency optimization via caching/async/batching. Also developed a personalized recommendation system for the Frenzy social platform and partnered closely with product/UX to define metrics and iterate on hybrid recommenders and cold-start handling.”

Raunak Bandyopadhyay

Screened

Intern-level Full-Stack Engineer specializing in AI and web systems

Amherst, MA0y exp

“Backend-focused intern in telehealth who combined engineering, QA leadership, and emergency infrastructure ownership. Built a text chat feature and an OpenAI-powered therapy chatbot, then stepped up to rebuild AWS infrastructure after it was accidentally deleted, while also improving chat responsiveness and reporting a 30% engagement lift.”

keerthana bhukya

Screened

Junior Software Engineer specializing in AI/ML and cybersecurity

Stockton, CA2y exp

“Salesforce-focused engineer with hands-on depth across Sales Cloud, Service Cloud, Apex, LWC, and Aura. Stands out for owning end-to-end automation features, making thoughtful async architecture decisions to balance performance and reliability, and designing responsive Lightning interfaces that hold up under large data volumes.”