Hybrid· Full-time· Permanent· 12+ years of industry experience required2026-10-26
Machine Learning Engineer - Global Sourcing & Supply Management (GSSM) Solutions
Apple Bengaluru, Karnataka, IndiaSupply Chain AI
Salaire : non communiqué
Apple is recruiting a Machine Learning Engineer for Global Sourcing & Supply Management (GSSM) Solutions within its Operations and Supply Chain organization in Bengaluru, Karnataka, India. The official job posting is dated September 1, 2026, carries role number 200681175-0321 and was verified as open on October 26, 2026, with a Submit Resume option available.
According to Apple's posting, the role involves designing and deploying machine learning models and AI systems for forecasting, optimization, decision-making and intelligent workflow automation across supply chain operations. It covers GenAI, large language models, structured reasoning, Text-to-SQL, production deployment and MLOps, with close collaboration between engineering teams and business stakeholders.
The posting specifies a minimum of 12+ years of industry experience. Salary information is not disclosed in the supplied posting details. No remote, hybrid or on-site work arrangement is asserted. The role indicates investment in internal AI capabilities for sourcing and supply-management decisions, not independently verified improvements in operational performance.
Illustration disclosure: the image displayed in the company-logo position is a thematic Pexels stock photograph of port containers and machinery. It is not Apple's official logo, does not depict Apple facilities or operations and does not imply endorsement by Apple or the photographer.
L'angle Dataleo
This Apple position connects machine learning engineering directly with sourcing and supply-management workflows rather than treating AI as a standalone experimentation function. The affected decisions include how forecasts, optimization results and structured analysis inform supplier planning and operational commitments. The opportunity lies in turning ambiguous business questions into validated models and reliable decision-support processes. Value depends on production-quality engineering, measurable outcomes and clear boundaries between model recommendations and authorized sourcing actions. The main failure mode is delivering technically sophisticated GenAI or ML applications that produce plausible outputs without sufficiently reliable evidence, operational integration or accountability. The critical question is how model outputs become dependable sourcing decisions and who retains authority over supplier commitments. The posting indicates a hiring priority, not proven operational improvement.