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Vetted PyTorch Professionals
Pre-screened and vetted.
Senior Software Engineer specializing in AI systems and real-time distributed platforms
Staff Full-Stack Software Engineer specializing in cloud platforms and real-time health data
Staff Data Scientist specializing in machine learning, deep learning, and big data
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Junior Software Engineer specializing in AI/ML and recommendation systems
Executive AI/ML Engineer specializing in LLMs, NLP, and production ML systems
Senior AI Engineer specializing in NLP and large language models
Richard Jackson
Senior Machine Learning Engineer & Solution Architect specializing in cloud AI systems
“Backend/ML platform engineer with Google experience leading Python microservices for an AI-driven recommendation/retrieval system, including PyTorch inference and a retrieval-augmented generation workflow. Strong in production Kubernetes + GitOps (ArgoCD), real-time Kafka/Spark pipelines, and phased on-prem/legacy to AWS/GCP cloud migrations with reliability-focused rollout and rollback practices.”
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
Staff Full-Stack Software Engineer specializing in cloud platforms and healthcare data pipelines
Senior Machine Learning Engineer specializing in Generative AI and NLP
Senior Full-Stack Software Engineer specializing in AI-powered distributed systems
Chase Bradley
Staff AI Engineer specializing in LLM systems, retrieval, and ML infrastructure
“ML/LLM engineer from Cohere who has owned retrieval, reranking, agentic workflows, and internal evaluation infrastructure end-to-end in production. Particularly strong in turning brittle RAG and research-heavy ideas into scalable enterprise systems with grounded outputs, lower customer escalations, and adoption by major clients like Notion and Fujitsu.”
Jesse Olson
Executive AI technology leader specializing in agentic AI and enterprise transformation
“Candidate is exploring a startup in the autonomous agentic AI space and is approaching it with a disciplined founder mindset. They are evaluating market opportunity, competition, customer pain, ROI, technical feasibility, and MVP options while already speaking with potential customers across verticals to validate needs and buying processes.”
Andrew Coven
Executive Software Engineering Leader specializing in AI/ML products and large-scale platforms
“Engineering leader who drove Lightroom Mobile’s roadmap at Adobe by enforcing reuse of Photoshop imaging tech to deliver RAW, non-destructive mobile editing with full Creative Cloud compatibility. In startup environments, has repeatedly scaled and professionalized engineering orgs (career ladders, evaluation systems, delivery process) and led high-stakes cloud/platform transformations—migrating off Heroku and consolidating fragmented AWS/GCP usage into a single GCP platform—improving reliability, deploy speed, and cutting cloud costs by 20%+.”
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.”