Vetted Reinforcement Learning Professionals

Kanishka Harwani

Screened ReferencesModerate rec.

Intern Robotics & Embedded Systems Engineer specializing in ROS2 autonomous mobile robots

New York, NY1y exp

“Recent graduate with a prototyping/hardware engineering background who is building a ROS2-dependent swarm robotics system for transporting non-standard cargo using 3+ robots. Has hands-on experience integrating multiple sensor modalities (LiDAR, IMU, GPS, radar) and developing custom teleoperation ROS nodes, currently tackling networking/communication challenges in multi-robot coordination.”

“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”

Gagan Mundada

Screened

Intern Machine Learning Engineer specializing in multimodal AI and evaluation benchmarks

San Diego, CA2y exp

“ML-focused candidate with beginner ROS/ROS2 experience (custom pub-sub nodes; TurtleBot3 SLAM simulation debugging via topic inspection and transform/orientation checks). Has research/project exposure to LLM training approaches (GRPO with pseudo-labels using Hugging Face TRL on Qwen/Llama) and uses Docker/Kubernetes + CI/CD to run ViT saliency-attention/compression workloads on UCSD Nautilus infrastructure.”

“Robotics software engineer focused on ROS2 motion and calibration systems—built a trajectory generator/low-level controller using TOPPRA that improved robot motion speed by 11x while increasing accuracy. Experienced making high-frequency robot communication more real-time (core isolation) and shipping ROS2 modules via Docker-backed CI/CD, including serving as release manager coordinating reviews, release notes, and QA.”

Rathin Shah

Screened

Senior Robotics Systems Engineer specializing in autonomous mobility and optimal control

Pittsburgh, PA6y exp

“Robotics technical lead who architected and built a high-speed autonomous lunar rover mobility software system for GPS-denied environments, integrating MPC/LQR control, trajectory optimization, state and slip estimation, terrain-aware planning, and perception. Has deployed Deep RL policies trained in NVIDIA Isaac Sim onto real rover hardware via a ROS2 inference-node interface, with strong focus on real-time performance profiling, sim-to-real, and safety/HIL testing.”

Emmanuel Haldopoulos

Screened

Senior Systems Integration Engineer specializing in ADAS and autonomous driving

San Jose, CA5y exp

“Solutions engineer at a Voice AI startup building and deploying voice agents for self-storage customers, owning the full lifecycle from cold outreach and demos through contract negotiation, FMS/API integration, and post-sales optimization. Also has Audi/Porsche experience demoing pre-production vehicles and autonomous driving features (L3 lane changes) to internal stakeholders and VW Group leadership, with a strong track record of tailoring technical narratives to varied audiences and improving conversion by adapting agents to regional dialects.”

Benjamin Kozel

Screened

Mid-level Machine Learning & Software Engineer specializing in RAG systems and ML infrastructure

Atlanta, GA4y exp

“Built and deployed an in-house RAG LLM system ("MONTY") using LLaMA 3B + FAISS to help teams quickly understand long internal/external specifications. Delivered usable production performance despite severe compute limits (single RTX 3080) by tuning retrieval/reranking and model choice, and is planning a LightRAG/knowledge-graph rewrite to improve accuracy and latency.”