Vetted Reinforcement Learning Professionals

“Analytics-focused candidate with hands-on experience using SQL and Python to clean messy business data, automate reporting, and build practical customer analytics solutions. Notable examples include a 70% reduction in reporting time through Python-based Excel automation at Shell and stakeholder-friendly retention/RFM segmentation work for small business clients in freight and winery contexts.”

Atharva Bhide

Screened

Entry Software Engineer specializing in AI/ML and multimodal systems

Los Angeles, CA1y exp

“Built and shipped a production healthcare AI platform for a clinic in Brea, LA that combined LLM-based clinical report generation, voice agents for appointment workflows, and camera-based patient monitoring. Stands out for pairing multimodal AI architecture with production-grade reliability and compliance practices, while delivering concrete business results including 90% workflow automation, 200 hours saved per month, and a 60% improvement in customer retention.”

“Electrical design engineer with hands-on experience leading industrial and commercial building power systems, including a 4.16kV redundant distribution design for a 24/7 automotive supplier facility that was installed without shutting production down. Brings a strong mix of NEC-driven design rigor, analysis tools like AutoCAD Electrical/Revit/MATLAB, and field-oriented collaboration across controls, mechanical, commissioning, and contractor teams.”

“ML/AI engineer with hands-on ownership of a production LLM/RAG system at Goldman Sachs, focused on workflow automation and large-scale document search for operational teams. They combine strong MLOps and backend engineering skills with practical GenAI evaluation and safety practices, and cite measurable impact including 22% better task guidance accuracy and sub-second search across millions of records.”

“Robotics software engineer with hands-on experience across reinforcement learning and ROS/ROS2, including a project teaching Boston Dynamics Spot to open a door by combining vision-based pose estimation with SAC-trained IK and a walking policy in MuJoCo. Previously built ROS Noetic control for surgical robots using RCM with MoveIt IK and achieved sub-0.02s latency via threading; also participated in a NASA ROS2 space simulation building rover teleop and sensor-driven mapping.”

“Early-career robotics engineer who has integrated LLM/NLP command interfaces (OpenAI/LLaMA) into ROS-controlled industrial manipulators and built data-driven controls for underwater soft robotic actuators. Combines hands-on fabrication (balloon actuator with embedded copper traces) with sensor debugging (IMU/Aurora) and simulation work in Gazebo, with practical exposure to edge deployment constraints on Jetson Nano and model quantization.”

“Robotics software engineer who owned the end-to-end navigation stack for a mobile manipulation robot (Cone-E), integrating ZED-2i SLAM into a real-time occupancy grid with live obstacle avoidance, A* planning, and lookahead control. Strong in real-time debugging and stability improvements (goal snapping/locking, obstacle persistence, rate-limited replanning) and validates changes on hardware, supported by simulation (Gazebo/Webots) and Docker/CI-based testing.”

“Head of Robotics at Orbital Composites who built the full ROS-based software stack ("OrbOS") for multi-robot KUKA-arm 3D printing of carbon-fiber composite geometries. Deep in motion planning and real-time control—created a pipeline to turn underconstrained toolpaths into optimal joint trajectories at massive command scale, plus redundant safety gatekeeping and a 250Hz synchronized robot+extruder controller. Has operated in a technical-founder capacity for ~6 years and values an engineering culture of openness, honesty, accountability, and high ownership.”

Jayanti Lahoti

Screened

Junior Full-Stack Software Engineer specializing in AI and cloud-native systems

San Diego, USA2y exp

“Backend/systems-oriented engineer focused on building production-constrained LLM agent workflows that automate repetitive operator tasks via intent/entity extraction, retrieval grounding, and structured action recommendations with human-in-the-loop review. Emphasizes reliability through deterministic orchestration, strict tool/function schemas, observability, and disciplined evaluation/feedback loops, with strong experience handling messy multi-service operational data and idempotent execution.”

Krishna Kodur

Screened

Mid-level Robotics & AI Researcher specializing in human-robot interaction and reinforcement learning

Santa Clara, CA8y exp

“Robotics software engineer who built an end-to-end mobile manipulation platform (Franka Panda on a Clearpath Ridgeback) for a simulated-kitchen human-robot interaction study with natural speech commands, implemented in Python/ROS. Has hands-on experience integrating diverse sensors (RealSense, LiDAR, biosignals) with deep learning frameworks (PyTorch, Hugging Face) and fine-tuning GPT-Neo, plus simulation (Gazebo) and modern deployment practices (Docker/Kubernetes, CI/CD).”

“Data/ML engineer with end-to-end ownership experience at Charles Schwab, spanning data ingestion, anomaly detection, data quality infrastructure, and dashboards used daily by compliance and business teams. Stands out for debugging complex cross-layer issues in systems processing 17M+ records per day and for turning one-off data quality checks into reusable frameworks that scaled across business units.”