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Vetted Deep Learning Professionals
Pre-screened and vetted.
Senior AI/ML Engineer & Data Scientist specializing in NLP, entity resolution, and knowledge graphs
Mid-level Machine Learning Engineer specializing in LLMs, ranking, and scalable ML systems
Intern Machine Learning Engineer specializing in optimization, federated learning, and LLMs
Mid-level Technical Solutions Architect & Technical Operations Manager specializing in AI automation
Intern Hardware/Robotics Engineer specializing in autonomous systems and test automation
Intern Robotics Software Engineer specializing in perception and ML systems
Mid-level AI/ML Engineer specializing in Generative AI agents and FinTech risk systems
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal recommendation systems
Mid-level Machine Learning Engineer specializing in LLM personalization and scalable MLOps
Intern/Junior Robotics & Controls Engineer specializing in autonomy and embedded systems
Principal Data Scientist specializing in AI and quantitative investment strategies
Mid-level Software Engineer specializing in real-time backend systems and FinTech payments
Intern Data Scientist specializing in machine learning and trustworthy AI
Mid-level Machine Learning Engineer specializing in real-time recommender systems and MLOps
Intern Machine Learning Engineer specializing in LLM systems and recommendation/search
Intern Machine Learning Engineer specializing in systems, kernels, and GPU computing
Senior Data Scientist specializing in large-scale ML systems and recommendations
Mid-level AI/ML Engineer specializing in NLP, computer vision, and recommender systems
Brian Sanders
Senior Backend Engineer specializing in GenAI, LLMs, and scalable data pipelines
“Backend/ML platform engineer from Snapsheet who owned production Python services and data pipelines for insurance claims, including an AI document classification/summarization FastAPI service on ECS/Fargate processing 1M+ documents/year. Strong in AWS infrastructure (Terraform, CI/CD, secrets/IAM, autoscaling), Glue/PySpark ETL with schema evolution controls, and legacy SAS-to-microservices modernization with safe, feature-flagged rollouts and measurable performance wins.”