UP.Labs

HQ
Santa Monica
Total Offices: 2
40 Total Employees
Year Founded: 2021

Jobs at UP.Labs

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Recently posted jobs

Angel or VC Firm • Automotive • Transportation
Lead end-to-end deployments of AI for hotel revenue teams: integrate PMS/CRS/RMS, ship ML/optimization models and agentic workflows, validate recommendations with revenue managers, define metrics, drive adoption, and hire/mentor the forward deployed engineering team.
Angel or VC Firm • Automotive • Transportation
Lead and build the engineering organization for an AI-driven industrial commerce platform. Own architecture, data pipelines, ML and computer vision integrations, mobile sensor-enabled identification, enterprise system integrations (ERP/DMS), cloud infrastructure, CI/CD, and observability. Hire and mentor distributed teams, deliver rapid MVPs, and establish a scalable technical vision for identification, predictive enrichment, and commerce execution.
8 Days AgoSaved
Remote
USA
Angel or VC Firm • Automotive • Transportation
The Founding Engineer will develop full-stack applications, shape product direction, and work in ambiguous environments from concept to validation.
Angel or VC Firm • Automotive • Transportation
Lead backend architecture and system design for an AI-driven manufacturing planning platform. Build and scale a Node.js/Python stack with Azure Databricks, implement optimization and scheduling algorithms, establish engineering patterns, and mentor/lead a small early-stage engineering team while collaborating with product and data functions.
22 Days AgoSaved
Remote
USA
Angel or VC Firm • Automotive • Transportation
Lead Data Science initiatives, build AI/ML solutions, and mentor teams in a collaborative environment, driving innovation in a leading construction equipment venture.
23 Days AgoSaved
Remote
USA
Angel or VC Firm • Automotive • Transportation
Part-time QA Analyst reviews emails, invoices, confirmations, and system outputs to verify financial and operational data accuracy. Identify inconsistencies, duplicates, and mapping errors, flag anomalies, and report reproducible issues with context to engineering to maintain data integrity and reliable financial outputs.