Lead AI Engineer, Data Solutions

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

$208K-$286K From the posting source checked Jun 20, 2026

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

112% above the BLS role benchmark for software engineering aggregate.

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

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, Data Solutions California - San Francisco; Washington - Seattle; Illinois - Chicago; New York - New York We are looking for a Lead AI Engineer to build next-generation AI and ML systems at Salesforce. This role focuses on developing intelligent decisioning systems and building an agent flywheel-a system of feedback loops that continuously evaluate, optimize, and improve agent performance over time. This is an applied AI role with strong data and systems ownership. You will build models and agents and the data pipelines and evaluation loops that enable continuous learning in production. What You'll Do Build the Agent Flywheel Design feedback loops that enable agents and ML systems to improve from real-world outcomes Track outcomes (engagement, conversion, quality) and evaluate agent performance Build pipelines that collect and structure agent traces into training and evaluation datasets Drive continuous improvement via prompting, policies, model selection, and fine-tuning Develop ML & Agent Systems Build and deploy ML models (classification, ranking, forecasting, recommendation) Design AI agents that combine LLM reasoning, tool usage, and ML decisioning Implement reusable patterns for multi-step reasoning, tool orchestration, and structured outputs Integrate models and agents into business-critical workflows Own Data & Model Pipelines Design and build scalable data pipelines (batch and near real-time) for training, evaluation, and inference Transform raw interaction data into features, labels, and evaluation datasets Enable continuous retraining and evaluation through tightly coupled data + model pipelines Ensure data quality, consistency, and reliability Evaluation & Experimentation Build offline and online evaluation frameworks Develop

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