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Vetted Reinforcement Learning Professionals
Pre-screened and vetted.
Meredith Ma
Entry-level AI/ML Software Engineer specializing in generative AI and computer vision
“Built and owned a production RAG coding assistant at Magna International used by 200 engineers, with hands-on work across React/TypeScript, retrieval infrastructure, and Postgres observability. Also brings an unusual blend of product UX thinking from AR game onboarding work, showing strength in both technical systems reliability and user activation.”
Ankit Aggarwal
Intern Robotics Engineer specializing in ROS, motion planning, and embedded systems
“Robotics software engineer who delivered the Lunar ROADSTER—an autonomous bulldozing rover for lunar terrain manipulation—building the control system, path planning, and perception in ROS 2. Implemented crater detection using a YOLO model fused with ZED stereo depth to recover crater geometry, and structured autonomy around ROS 2 actions integrated into an FSM with CI/CD-backed system testing. Also has industrial robotics experience controlling a Fanuc arm for additive manufacturing and building ROS interfaces for PLC I/O.”
Vishal Mittal
Director-level Engineering Manager specializing in cloud security platforms and AI-driven automation
“Senior engineering leader in the Bay Area with experience spanning VMware, Hortonworks/Cloudera, Barracuda, and Palo Alto Networks, including leading open-source work (Apache Knox) and architecting large-scale security platforms. Has driven disaster recovery and cloud security products, designed Python microservices for Microsoft 365 security, and scaled teams (3x) while formalizing enterprise readiness practices with automated documentation using Notebook LLM.”
Chris Du
Intern Full-Stack Software Engineer specializing in web apps and AI systems
“Product/UX designer who builds end-to-end systems across both consumer wellness and industrial/technical domains. Designed BloomPath (mental-wellness platform for therapists and young professionals) using research-driven, emotionally safe interaction patterns, and also simplified a Bosch autonomous parking vision-language mapping pipeline into a developer-facing real-time UI with layered debug tooling. Comfortable collaborating deeply with engineers and contributing in React/JS.”
Ranjani Salla
Mid-level AI/ML Engineer specializing in LLMs, FinTech, and Healthcare IT
“Built production GenAI systems in both healthcare and financial services, including a Verily clinical platform and an Accenture financial Q&A product. Stands out for combining advanced RAG, fine-tuning, safety evaluation, and infrastructure engineering to deliver measurable gains in engagement, groundedness, hallucination reduction, and cost efficiency.”
Mason McBride
Junior Software Engineer specializing in AI, game theory, and blockchain protocols
“Backend engineer who built gnocal, a ~150-line stateless Go service that turns on-chain event data into standards-compliant .ics calendar feeds consumable by Apple/Google Calendar, deployed on Fly.io. Also refactored MCTS into Monte Carlo Graph Search (Python-to-Rust) using deterministic tests and state canonicalization to handle transpositions, and implemented decentralized role-based ACLs in Gno for a smart-contract web hosting network (gno.land / All in Bits).”
Swarnabha Roy
Mid-level Robotics & Computer Vision Engineer specializing in autonomous systems and edge AI
“Robotics/perception researcher (MVOS Lab, South Dakota State University) who built an end-to-end multimodal RGB-D + LiDAR pipeline for autonomous greenhouse harvesting and 3D plant phenotyping. Demonstrated strong production ownership by diagnosing motion blur with ROS-bag + OpenCV metrics and shipping an edge-deployed, scan-quality-aware workflow that boosted barcode read rate to 98% and supported ~70% autonomous pepper detection/harvesting accuracy.”
Daniel Luzzatto
Junior Machine Learning Engineer specializing in LLMs, computer vision, and robotics
“Built and deployed an agentic, multimodal LLM system that automates privacy redaction pipelines (audio/video/tabular) using LangChain orchestration and a closed-loop self-correction design. Personally implemented and performance-optimized core CV tooling (face blurring with tracking/Kalman filter) achieving >100 FPS on CPU, and validated reliability with golden-dataset benchmarking across 100+ privacy intents and measurable redaction metrics.”
Samuel Prabhakar Vara
Mid-level AI Engineer specializing in machine learning and healthcare research
“Backend engineer with end-to-end ownership of scientific and AI-powered systems, including neuron imaging pipelines at Monell Chemical Senses Center and an LLM-based structured information extraction platform for Wharton and PSG. Stands out for turning messy, compute-heavy workflows into reliable production backends with measurable impact, including saving researchers over 50 hours per week.”
Mid-level Machine Learning Engineer specializing in deep learning, MLOps, and real-time inference
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and recommendation systems
Intern AI/ML Engineer specializing in LLM systems and cloud-native microservices
Junior Robotics & AI Engineer specializing in autonomous navigation and embodied learning
Junior AI/ML Engineer specializing in agentic AI and cloud optimization
Intern Perception/Robotics Engineer specializing in computer vision and embodied AI
Junior Software Engineer specializing in ML, computer vision, and data engineering
Mid-level Data Engineer specializing in AWS ETL and data warehousing
Mid-level Robotics Engineer specializing in autonomy, computer vision, and reinforcement learning
Mid-level Machine Learning Engineer specializing in LLM inference and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Staff Full-Stack & AI Engineer specializing in LLM agents, RAG, and cloud-native systems
Mid-level Data Scientist specializing in GenAI, NLP, and deep learning
Intern Machine Vision & Robotics Engineer specializing in computer vision and reinforcement learning