Lead AI Engineer
Salesforce - Mexico - Mexico City
Posted May 12, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
- Source-linked last checked May 7, 2026
- Salary
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- 401(k) match
- Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked May 7, 2026.
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Lead AI Engineer Mexico - Mexico City Lead AI Engineer (Mexico City) Data Solutions Org Hybrid We are looking for a Lead AI Engineer to drive the development of next-generation AI and ML systems at Salesforce. This role owns the design and evolution of intelligent decisioning systems and expands into building a broader agent flywheel (a system of self-improving feedback loops that continuously evaluate, optimize, and evolve agent performance). This role sits on the applied side but requires strong data and systems engineering depth - you will build not just models and agents, but the data pipelines, evaluation loops, and lightweight system scaffolding that allow them to continuously improve in production. You will build production-grade ML models, embed them into agent workflows, and define how agents learn from real-world outcomes. This is a hands-on, high-impact role focused on shipping systems that directly influence agent performance, efficiency, revenue, and customer experience. What You'll Do 1) Build the Agent Flywheel Design and implement feedback loops that enable agents and ML models to self-improve over time Develop systems for: Outcome tracking (e.g., engagement, conversions, resolution quality) Agent evaluation (LLM + deterministic + human-in-the-loop signals) Iterative optimization (prompting, policies, model selection, fine-tuning) Build pipelines that collect and structure agent traces (inputs, tool usage, intermediate steps, outputs) into high-quality training and evaluation datasets Close the loop from production signals → evaluation → model/prompt improvements 2) Develop Production ML & Agent Systems Build and deploy application-specific ML models (classification, ranking, forecasting, recommendation, etc.) Design and implement
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