Lead AI Engineer, Data Solutions
Role details
Job location
Tech stack
Job description
- 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
Requirements
Core Requirements
- 6+ years in AI/ML engineering or applied data science
- Strong Python experience in production systems
- Proven experience building and deploying ML models
- Experience building data pipelines (ETL/ELT, batch or streaming)
- Experience with APIs and backend systems
Agent & LLM Experience
- Experience with LLM-powered systems (prompting, orchestration, evaluation)
- Familiarity with agent workflows and tool usage
- Experience with evaluation loops, agent traces, or iterative improvement systems preferred
Data & Systems Expertise
- Experience building data pipelines supporting ML systems
- Familiarity with tools like Spark, Airflow/Dagster, Snowflake/BigQuery
- Understanding of data quality, lineage, and reproducibility
Modeling & Experimentation
- Strong understanding of supervised learning and evaluation methods
- Experience with A/B testing and experimentation
- Ability to design systems combining ML, LLMs, and business logic, * Experience with agent improvement systems (scoring, optimization loops)
- Exposure to evaluation tools (e.g., LangSmith, Braintrust, or similar)
- Experience with large-scale experimentation platforms
- Familiarity with enterprise SaaS or CRM
Benefits & conditions
benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. The typical base salary range for this position is $172,500 - $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 - $285,800 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.