Up Labs
Sr AI/ML Engineer (LATAM Remote)
USA · Senior · Full-time
Sponsorship not specifiedDetected 74 days ago
PythonVector DatabasesAWSGCPAzureCloud PlatformsMachine LearningData EngineeringComputer VisionLLMsRAGAgentic AILangGraphAI OrchestrationLogisticsRoboticsCommunication
About the role
- This role is best suited for someone who is comfortable operating in early-stage, ambiguous environments.
- Many projects will begin with a business problem, rough concept, or operational pain point rather than a fully defined technical specification.
- What You'll Work On You'll work on AI-driven applications involving image detection, object recognition, intelligent automation, and LLM-powered workflows.
Responsibilities
- Overview UP.Labs is a venture studio dedicated to building innovative startup companies from the ground up.
- We partner with leading corporations and entrepreneurs to identify major industry opportunities, validate new venture concepts, and turn early ideas into real products.
- You should be able to take a loosely defined problem, determine a practical technical path forward, make sound assumptions where details are missing, and drive toward a working solution.
- You'll also build LLM-based capabilities using modern orchestration frameworks, retrieval systems, tool-calling patterns, and agentic workflows.
- We partner with leading corporations and entrepreneurs to identify major industry opportunities, validate new venture concepts, and launch software and hardware companies from the ground up.
Benefits
- AI/ML Engineer to help build applied AI systems across computer vision, LLM-powered workflows, intelligent automation, and production AI infrastructure.
- One key product area involves using computer vision models to identify industrial objects, human interactions, defects, and operational patterns in manufacturing environments.
Apply directly at Up Labs →Create a free account for alerts like thisView Up Labs immigration profile
This listing is sourced directly from Up Labs's careers page and normalized into a canonical job model.