Description
Overview of the Role:
We are infusing AI into every aspect of our software engineering. While AI is writing the code, validation is what needs to be done to meet enterprise quality. We are looking for an enthusiastic Lead engineer who follows the AI first approach to build and maintain validation of the code and ensure it is production ready for our customers. This individual will be working on our recent acquisition SPIFF, a Sales rep compensation engine that solves a critical problem for the enterprise sales team. This engineer should have an AI first approach and be able to help the team translate from traditional testing to using AI in driving acceleration and accuracy in testing and be a multiplier using AI tools. Prior experience as a member of a Data Science, ML Science, or ML Engineering team is a plus.
As a System Quality LMTS, you will play a key role in defining, implementing, and maintaining the quality strategy for Spiff, a Salesforce acquisition and one of Sales Cloud growth engines, With a strong focus on integrating cutting-edge AI technologies. You will collaborate with cross-functional teams, including product development, data science, and AI teams, to ensure that Salesforce’s AI-driven solutions meet the highest quality standards for enterprise applications.
Key Responsibilities:
Drive Innovation & Adoption: Identify opportunities for integrating AI/ML solutions into existing workflows, propose new ones that are high-quality and efficient, and generally positively influence engineering decision-making by communicating and evaluating solution options, and facilitating agreement among key stakeholders. Ensure that we are continuously raising our standard of engineering excellence and producing high business impact.
Continued Excellence in AI/ML: Be tuned into developments in the field of AI/ML, by tracking publicly available models, frameworks, libraries and publications, while being focused on their practical application and relevance to Salesforce.
Technical Leadership on Proof of Concepts: Influence the direction of R&D as a whole through your technical, process, or product knowledge leadership. Industry-expert level in understanding of quality concerns and validation techniques. Able to drive behaviors and initiatives that both focus on quality and throughput.
Human Evaluation: Oversee human evaluation processes for subjective quality dimensions, such as response engagement, user safety, and contextual accuracy.
Define Quality Strategies: Develop and implement robust quality strategies for Salesforce’s SaaS products, focusing on AI/ML and LLM integrations.
Multiplier: Provide technical leadership for critical areas that significantly impact customer success. You have depth of expertise in key technologies and you are often consulted on the design and delivery of new solutions. You bring new best practices to R&D and actively ensure that they are being used. Continues to deepen and widen their understanding of the application and drives discussion of sometimes complex or controversial issues in open forums such as concept reviews, VAT discussions, and cross team discussions.
Cross-Platform Collaboration: Use your understanding of customers’ needs across industries and multiple technology landscapes (CRM, Modern Data Stack, Analytics & BI, CRM and AI) to develop solutions across Salesforce's technology stack. Work in a consultative fashion to improve communication, teamwork and alignment among teams inside and outside of the organization.
Mentor and Organization Builder: Be a cornerstone in the infrastructure of technical expertise represented by the organization's senior engineers. You challenge and engage with them to develop their expertise and leadership contributions. You are a key resource for engineers seeking to advance to the next level.
Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance
Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
Required Qualifications:
10+ years of experience of proven software development experience
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Strong JAVA skills: Strong technical fundamentals of Java particularly in data structures and algorithms. You can code and evaluate the code written by AI agents and able to provide feedback to scale the internal models to improve code quality
AI & Machine Learning Expertise: The candidate is also expected to be familiar with LLM and AI/ML techniques and practices to drive software validation and improve the pace of CICD pipelines.
LLM Expertise: Hands-on experience evaluating Large Language Models (LLMs) for quality response, including familiarity with GPT-like models and AI evaluation frameworks is a big plus
Focus on Delivery: Ability to deliver practical solutions for an active user base, which you are expected to follow-up with enhancements and improvements, as needed.
Collaboration & Communication: Excellent communication skills, with the ability to work cross-functionally with product managers, engineers, AI/ML engineers, and data scientists.
Problem-Solving & Analytical Skills: Strong analytical and problem-solving abilities with a focus on identifying quality gaps and driving improvement.
A demonstrated, genuine AI-first approach to engineering. Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.
Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows
Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.
A related technical degree required.
Preferred Qualifications:
A software engineer with strong coding background and understanding of delivering software at pace in Agentic PDLC
A strong experience of white boxing and fixing code is a huge plus
Strong understanding of public cloud infrastructure - AWS/Azure/GCP; Certification in Salesforce.
Can design, implement, test and deliver AI/ML-based validation frameworks for highly scalable products.
Fluent with automation technologies such as JUnit, Jest, Selenium and Jenkins
For roles in San Francisco and Los Angeles: 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.