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Vetted CUDA Professionals
Pre-screened and vetted.
Mid-level Robotics & AI Engineer specializing in Reinforcement Learning and Sim-to-Real
Junior Software Engineer specializing in full-stack and data infrastructure
Junior Robotics Software Engineer specializing in ROS 2 autonomy and perception
Mid-level Robotics Software Engineer specializing in perception, SLAM, and autonomous navigation
Entry-level Machine Learning Engineer specializing in LLM systems and RAG
Mid-level Machine Learning Researcher specializing in Cheminformatics & HPC
Senior Perception & Sensor Fusion Engineer specializing in LiDAR/Radar for autonomous systems
Entry-Level Software Engineer specializing in ML and Full-Stack Development
Junior Robotics Software Engineer specializing in autonomous navigation and perception
Junior Computer Engineering researcher specializing in robotics, autonomy, and computer vision
Junior Machine Learning Engineer specializing in LLM training and high-performance inference
Mid-level Software Engineer specializing in AI, reinforcement learning, and robotics
Intern Data Scientist specializing in machine learning, forecasting, and LLM applications
Intern Data Engineer specializing in cloud data pipelines and network security research
Junior Software Engineer specializing in AI/ML and full-stack development
Junior Software Engineer specializing in distributed systems and AI
Senior Full-Stack Software Engineer specializing in FinTech and payments
vedavathi thumula
Mid-level GenAI/ML Engineer specializing in agentic AI and RAG systems
“Backend/platform engineer who has owned a Python/FastAPI results API and deployed it on Kubernetes with Helm and GitHub Actions-driven CI/CD. Demonstrates strong production operations mindset across performance tuning, monitoring, safe rollouts/rollbacks, and phased migrations, plus hands-on Kafka streaming experience focused on ordering and idempotency.”
Ankita Mungalpara
Mid-level Data Scientist specializing in Generative AI and multimodal systems
“Recent J&J intern who built a conversational RAG agent and led a shift from a monolithic model to a modular RAG workflow, cutting response time from several days to under a second by tackling data fragmentation, context retention, and embedding/latency optimization. Also worked on a large (7B-parameter) multimodal VQA pipeline for healthcare research and stays current via NeurIPS/ICLR and open-source contributions.”