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Vetted Reinforcement Learning Professionals
Pre-screened and vetted.
Chenghui Cai
Director of Applied Sciences specializing in reinforcement learning and agentic AI for finance
“Embodied AI/robotics ML engineer with hands-on experience deploying POMDP-based reinforcement learning controllers on real mobile robots and vehicle fleets. Strong in sim-to-real robustness (domain randomization) and production rollout practices (HIL, shadow-mode, canaries, safety instrumentation), and has published related work (mentions a NeurIPS paper).”
Seongjae Ahn
Intern/Junior Robotics & Controls Engineer specializing in simulation, teleoperation, and diffusion policies
“Robotics software engineer focused on simulation-to-teleoperation pipelines in NVIDIA Isaac Lab/Isaac Sim, including custom Dynamixel motor control integrated with USD/physics for dataset collection. Has hands-on ROS2 Humble + MoveIt2 integration for UR + Robotiq in Omniverse and builds Docker/CI workflows for GPU-enabled robotics stacks; also brings MPC coursework and multi-robot ocean drone comms experience (XBee/I2C).”
Ege Ozgul
Junior Embedded/Robotics Engineer specializing in AI diagnostics, simulation, and real-time control
Soma Meghana Prathipati
Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection
“ML/robotics engineer with Apple experience who built a computer-vision-driven industrial defect detection system integrating a robotic arm with ROS-based real-time inference on an edge GPU. Drove major performance gains (cut inference time ~60% via quantization + TensorRT) and improved robustness to lighting/material variation, with strong emphasis on production reliability (health checks, watchdogs, observability, CI/CD) and interest in shaping early-stage startup engineering culture.”
Greta Skousgard
Senior Change Management & Recruiting Leader specializing in global adoption and talent operations
“People leader with large-team management experience (15 direct reports) and recruiting leadership responsibilities (4 indirect) spanning onboarding, coaching, performance management, and KPI delivery. As a Sr Recruiter, stepped in as a change facilitator to turn around adoption of a new scheduling system, boosting engagement by 50%+ through retrospectives and working sessions. Also partnered with senior leadership at McKinsey on AI product adoption communications to drive alignment across functions.”
Mid-level Software Engineer specializing in robotics, AI, and full-stack systems
Senior Machine Learning Engineer specializing in NLP, LLMs, and scalable ML platforms
Intern/Junior Robotics & Computer Vision Engineer specializing in industrial automation
Mid-level Machine Learning Engineer specializing in real-time fraud detection and edge AI
Staff AI/ML Engineer specializing in NLP, recommender systems, and Generative AI
Executive AI Platform & Innovation Leader specializing in Banking, GenAI, and AI Governance
Senior AI/ML Engineer specializing in LLMs, NLP, and production ML systems
Mid-level Machine Learning Engineer specializing in MLOps, RAG, and real-time personalization
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and real-time recommendation systems
Principal AI Architect specializing in GenAI, agentic systems, and RAG
Mid-level Machine Learning Engineer specializing in reinforcement learning and multimodal AI
Mid-Level Software Engineer specializing in cloud platforms, ML/GenAI, and distributed systems
Senior Data Scientist specializing in AI/ML platforms for finance and healthcare
Director-level Wireless Software Engineer specializing in 3GPP NAS protocol stacks (LTE/NR)
Mid-level AI/ML Engineer specializing in GenAI agents and production ML systems
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level Data Scientist/ML Engineer specializing in LLMs, NLP, and recommender systems