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Vetted Research Assistants
Pre-screened and vetted.
Junior Machine Learning Engineer specializing in LLMs and retrieval-augmented generation
Mid-level Technical Product Manager and R&D Software Engineer specializing in AI products
Intern Perception/Robotics Engineer specializing in computer vision and embodied AI
Mid-level Robotics Engineer specializing in autonomy, computer vision, and reinforcement learning
Senior User Operations & Support Engineer specializing in AI and Azure cloud platforms
Intern Machine Vision & Robotics Engineer specializing in computer vision and reinforcement learning
Mid-Level Software Engineer specializing in cloud-native microservices and AI/ML
Mid-Level Full-Stack Software Engineer specializing in cloud-native microservices
Senior Robotics Systems Engineer specializing in manipulation, motion planning, and real-time control
Intern Mechanical Engineer specializing in robotics, controls, and manufacturing
Intern Data Scientist specializing in product analytics, causal inference, and NLP
Mid-level Software Engineer specializing in data platforms and full-stack systems
Junior AI/ML Engineer specializing in NLP, LLM evaluation, and Azure MLOps
Mid-level Software Development Engineer specializing in AWS incident management and AI analytics
Mid-level Robotics & Machine Learning Engineer specializing in vision-language-action policies
Executive Finance & Operations Leader specializing in high-growth SaaS and FinTech
Daniel Storfer
Senior HR Business Partner specializing in employee relations and performance management
“HRBP/Employee Relations professional with Accenture and Clyde & Co experience, combining strong people analytics with risk-focused change management. Notably coded and analyzed four years of exit interview data to drive targeted retention and compensation actions across offices, and partnered with North America HR/Finance leadership on shared-services transition planning involving protected-class risk analysis.”
Jianing Gu
Junior Digital Marketing Specialist specializing in social media growth and performance marketing
“Growth-creative marketer with hands-on paid social and short-form video experience across restaurant and beauty (Tom Ford Beauty). Runs structured creative experiments (Meta A/B ad sets, Meta Pixel conversion tracking) and adapts strategy to platform behaviors (TikTok comments vs IG sharing). Has led creators/editors and drove a reported 100% ROAS lift on a Valentine’s Day TikTok ads iteration.”
Hitesh Bhojwani
Mid-level Robotics Software Engineer specializing in SLAM and 3D computer vision
“Robotics software engineer focused on outdoor mobile robot localization and navigation, building ROS1/ROS2 systems with NavSat+EKF sensor fusion and custom Nav2/Costmap2D extensions for 3D obstacle clearance. Demonstrates strong real-world troubleshooting by tracing localization drift to a failing IMU connector, repairing it, and then creating sensor-health monitoring tooling; experienced taking features from Gazebo simulation through field testing to Docker/Kubernetes deployment with CI via GitHub Actions.”
Narasimha Jwalapuram
Mid-level Applied AI Engineer specializing in LLM agents, RAG, and model alignment
“Applied Scientist with legal-tech experience who builds production LLM systems. Created and deployed Quibo AI, a LangGraph-based multi-agent pipeline that turns large markdown/Jupyter inputs into polished blogs and social posts, overcoming context limits via ChromaDB + HyDE RAG. Also built a large-scale iterative code-evolution workflow using multi-model orchestration (GPT/Claude/Gemini) with testing, debugging loops, and evaluation/observability practices.”
Pranav Puranik
Senior AI Engineer specializing in LLMs, RAG, and multimodal NLP
“Built a production LLM/RAG assistant for insurance/health claims agents that ingests 100–200 page patient PDFs via OCR (migrated from local Tesseract to Azure Document Intelligence) and delivers grounded claim detail retrieval plus summaries with PII/PHI guardrails. Experienced orchestrating large workflows with Celery worker pipelines and AWS Step Functions (S3-triggered, Fargate-based batch inference/accuracy aggregation), and collaborates closely with non-technical SMEs (claims agents/nurses) through shadowing, iterative demos, and SME-defined evaluation.”