Lead AI Engineer, Data Solutions at Salesforce – California - San Francisco | Jobyssey

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category

Software Engineering

Job Details

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

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 evaluation datasets, golden traces, and regression-style test sets
Run A/B experiments and track key metrics (quality, revenue impact, latency, etc.)
Use production signals to drive continuous optimization

Systems & API Development

Build scalable Python services and APIs powering agent workflows
Collaborate with platform teams while owning application-level systems
Ensure reliability, observability, and performance

Qualifications

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

Preferred Qualifications

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

What Success Looks Like

Agents and ML models improve continuously via feedback loops
Reliable data and evaluation pipelines power the agent flywheel
Measurable impact on business metrics (conversion, revenue, efficiency)
Fast, safe iteration enabled by strong evaluation systems

Unleash Your Potential

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.

Accommodations

If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form.

Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.

Posting Statement

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, 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.

Explore more jobs

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 evaluation datasets, golden traces, and regression-style test sets

Run A/B experiments and track key metrics (quality, revenue impact, latency, etc.)

Use production signals to drive continuous optimization

Systems & API Development

Build scalable Python services and APIs powering agent workflows

Collaborate with platform teams while owning application-level systems

Ensure reliability, observability, and performance

Qualifications

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

Preferred Qualifications

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

What Success Looks Like

Agents and ML models improve continuously via feedback loops

Reliable data and evaluation pipelines power the agent flywheel

Measurable impact on business metrics (conversion, revenue, efficiency)

Fast, safe iteration enabled by strong evaluation systems