Lead AI Engineer

Benefits

Parental leave

26 weeks From the posting source checked Jun 20, 2026

Non-birth-parent leave

12 weeks From the posting source checked Jun 20, 2026

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Not verified

Verification

Source-linked checked May 7, 2026

Salary

Not verified

401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Work mode

Hybrid From the posting source checked Jun 20, 2026

In-office days

2 days From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

Not verified

Company

Company stage

Public-company From the posting source checked Jun 20, 2026

Equity

Offered Verified - SEC 10-K source checked Jun 20, 2026

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

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

Read the full description at careers.salesforce.com. FewerJobs shows a preview and links to the original posting.