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Vetted Research Assistants
Pre-screened and vetted.
Mid-level Robotics Engineer specializing in ROS2 autonomy and simulation
Mid-Level AI Engineer specializing in LLM systems, GPU optimization, and multi-agent orchestration
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and cloud deployment
Mid-level AI/ML Engineer specializing in Generative AI agents and workflow automation
Junior Software Engineer specializing in backend, cloud, and LLM applications
Junior Software Engineer specializing in full-stack development and applied ML
Junior Robotics Software Engineer specializing in legged robotics and embedded control
Mid-Level Full-Stack Software Engineer specializing in scalable web platforms and APIs
Sree Gautham Alluri
Mid-level Robotics & AI Developer specializing in autonomous navigation and LLM-powered robotic systems
“Robotics Support Engineer at HAI Robotics supporting a 385-robot warehouse fleet at a Shein client site. Built a production automation and reporting workflow to diagnose and resolve abnormal shelf locations, cutting incidents from ~250/day to ~25/day while providing actionable root-cause data to client/ops/maintenance. Hands-on ROS 2 (Humble) debugging across Nav2/localization/TF and sensor integration issues including QoS and firmware coordination.”
Ann Emily Gregoire
Senior Frontend Developer specializing in React and industrial ERP systems
“Frontend engineer focused on scalable architecture and long-term maintainability, emphasizing modular component design in React + TypeScript. Has experience building reusable component libraries (including Storybook and shared repos) and collaborating with design/product to refine UX workflows, reducing development time over successive iterations.”
Junior Robotics and Computer Vision Engineer specializing in perception and autonomy
Junior Robotics & Mechatronics Engineer specializing in embedded automation and robotic systems
Executive People & Culture Leader specializing in Tech and FinTech
Diana Minine Gudinho
Mid-level Data Scientist specializing in GenAI, RAG, and forecasting
“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.”
Mid-Level Full-Stack Software Engineer specializing in microservices and Generative AI
Mid-Level Full-Stack Developer specializing in MERN and AR/VR applications
Executive AI Architect specializing in low-power edge/embedded AI systems
Himanshu Kiran Garud
Mid-Level Software Engineer specializing in full-stack web and data engineering
“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).”
Rithiwik Garuda
Mid-level Full-Stack Engineer specializing in cloud-native, event-driven data platforms
“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.”
Shashank Chauhan
Mid-level Software Engineer specializing in AI/ML and cloud data platforms
“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.”
Dara McNally
Junior Computer Vision Researcher specializing in deep learning and object detection
“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.”