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Vetted A/B Testing Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Junior Software Engineer specializing in AI/ML and recommendation systems
Staff Machine Learning Engineer specializing in search, ranking, and LLM systems
Senior AI/ML Engineer specializing in LLMs, recommendation systems, and ML platforms
Senior Full-Stack Software Engineer specializing in cloud-native web platforms and FinTech
Senior AI/ML Engineer specializing in LLM applications, RAG systems, and MLOps
Mid-Level Software Engineer specializing in Search, Ads, and Shopping systems
Staff Machine Learning Scientist specializing in NLP, LLMs, and Generative AI
Senior Software Engineer specializing in full-stack systems and ML-driven platforms
Staff AI/ML Engineer specializing in LLMs, fraud detection, and MLOps
Senior Data Engineer specializing in cloud data platforms and real-time streaming
Senior Full-Stack Software Engineer specializing in Mobile and AR/VR platforms
Staff Full-Stack Software Engineer specializing in cloud platforms and healthcare data pipelines
Senior Machine Learning Engineer specializing in Generative AI and NLP
Senior Software Engineer specializing in ML, distributed systems, and GenAI search
Senior Full-Stack Software Engineer specializing in AI-powered distributed systems
Nathan Telles
Staff Software Engineer specializing in distributed systems, cloud platforms, and AI services
“Meta engineer who owned end-to-end production systems for AI-enabled smart glasses, spanning React/TypeScript apps through Node/Java microservices on AWS EKS with Kafka/Postgres. Built and productionized a real-time RAG pipeline (LangChain + OpenAI + Elasticsearch) with rigorous guardrails (shadow/canary, fallbacks, monitoring), delivering major improvements in latency (~35–40%), error reduction (~30%), and engagement (reported +40% DAU).”
Neeraj Sharma
Executive Engineering Leader specializing in AI/ML platforms and cloud-scale distributed systems
“Senior engineering/technology leader with experience driving board-backed, multi-year platform transformations in retail commerce (American Eagle) and scaling ML/product delivery platforms at Amazon Alexa. Known for data-driven build-vs-buy decisions, org scaling (6→80), and measurable outcomes including 98.5%→99.9% uptime, 234 experiments generating ~$200M revenue lift, and increasing delivery velocity from 1.5 to 30 features/week while transitioning to DNN/large-model architectures.”
Danny Anop
Senior Full-Stack Engineer specializing in SaaS, e-commerce, and frontend platforms
“Frontend-leaning full-stack engineer who has built a multi-tenant AI-powered widget and admin dashboard platform used across 40+ merchant websites. Strong in TypeScript/Next.js/GraphQL systems design, reusable platform primitives, and cross-layer debugging, with a clear track record of shipping scalable product experiences under ambiguity.”
Christopher Sipola
Senior Machine Learning Engineer specializing in LLMs and recommendation systems
“ML/GenAI engineer who owned major parts of Spotify’s AI DJ from offline experimentation through deployment, monitoring, and iteration. They combine recommender systems, RAG, real-time feedback loops, and LLM safety/orchestration to ship consumer-facing personalization features that drove double-digit engagement and deeper listening sessions.”
Esha Pahwa
Intern Machine Learning Engineer specializing in LLM agents and multimodal reasoning
“LLM/agent engineer who built a production code-generation agent at Corvic AI that lets non-technical users query CSV/tabular data in natural language by generating and executing Python. Focused on making LLM systems reliable and scalable via schema-aware validation, sandboxed execution-feedback retries, prompt caching/embeddings, async execution, and high-throughput data processing with Polars; also partnered with Adobe product/marketing to ship brand-aligned AI content generation for email and push notifications.”
Somya Upadhyay
Senior Software Engineer specializing in cloud infrastructure and large-scale data pipelines
“Backend engineer on Amazon’s Geospatial Data team (Amazon Maps) who built a real-time road-layer service ingesting third-party and internal signals to deliver road closures/traffic overlays to delivery drivers on a ~3-minute cadence while minimizing mobile data egress. Demonstrates strong production reliability skills (rate limiting, idempotency, cache stampede prevention) and security depth (IAM, RBAC, tenant row-level security), plus careful handling of edge cases like manual override protection against automated feed overwrites.”