Director, Agentforce Engineering
Salesforce
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Job Description
Director, Agentforce Engineering at Salesforce
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
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’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.
The Role of Director, Agentforce Engineering
We are looking for a technical leader who understands that building an AI agent is only 10% of the work—the real engineering challenge is measuring it. We need a thought leader who can solve the "problem nobody talks about": evaluating non-deterministic agentic systems in production. You will lead the team responsible for defining what "good" looks like for agents, moving beyond basic accuracy to rigorous evals that bridge agent spec’s to business outcomes. You will thread together Applied Science (defining metrics, curation of golden datasets, establishing ground truth) and Product Engineering (shipping software).
Responsibilities
- Build the "Evaluation Core": Lead the engineering of a scalable evaluation platform that runs in parallel with agent execution.
- Thread Science & Engineering: Operationalize applied science by turning theoretical benchmarks into production regression tests and bring about a discipline of eval driven development.
- Thought Leadership: Lead as an expert on building Agents and deliver features that measure performance and accuracy of AI Agents. You are a builder and are a subject matter expert on agentic evals.
- You are an Engineering leader who can lead the group through technical leadership, process management, maintain a good discipline of high quality code delivery aided with AI tools as necessary.
- You are an Engineering leaders with deep technical expertise who operates with empathy and collaboration.
- You are a multiplier and have a passion for team and team members’ success providing technical guidance, career development, and mentoring.
Required Skills
- Specialized Agent Evaluation Experience: You have specific experience building evaluation harnesses for LLMs or Agents.
- Applied Science & Engineering Hybrid: You have a track record of managing "Research Engineering" or "Applied Science" teams where you had to operationalize vague scientific goals into shipping code. You are comfortable curating "Golden Sets" of data and building custom benchmarks from scratch.
- Deep Knowledge of Eval Methodologies: You are fluent in modern evaluation techniques, including: LLM-as-a-Judge: Validating judges against human ground truth to prevent self-bias. Behavioral Analysis: Evaluating how an agent thinks (Reasoning Traces/Chain of Thought), not just the final output.
- Production-Grade AI Experience: You have shipped AI products where you had to manage real-world constraints like token budgets, inference latency, and cost-normalized accuracy. Pragmatic orientation to building ML solutions that work in production at scale.
- Familiarity with academic and industry benchmarks and their limitations in a business environment.
- Experience building simulation environments (mock APIs, virtual users) to stress-test agents safely before deployment.
- Experience with Data engineering, specifically around data acquisition, creating data pipelines, metric measurement, and analysis.
- Experience owning highly available services and putting processes in place to maintain uptime.
- Prior experience working with global teams.
- Strong verbal and written communication skills, organizational and time management skills.
- Advanced degree in Computer Science, Machine Learning, or related field with a focus on system evaluation or reliability.
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.
Key skills/competency
- AI Agent Evaluation
- LLM Evaluation
- Applied Science
- Production AI
- Data Engineering
- Technical Leadership
- Simulation Environments
- System Reliability
- Performance Metrics
- Team Mentoring
How to Get Hired at Salesforce
- Research Salesforce's culture: Study their mission, values (Trust, Customer Success, Innovation, Equality, Sustainability), recent news, and employee testimonials on LinkedIn and Glassdoor to align your application with their 'Trailblazer' ethos.
- Customize your resume for Agentforce Engineering: Highlight experience in AI agent evaluation, LLM methodologies, applied science, data engineering, and leading teams delivering production-grade AI solutions to resonate with the specific needs of the Director, Agentforce Engineering role.
- Network strategically within Salesforce: Connect with current employees, especially those in AI, Machine Learning, or Engineering departments, on LinkedIn to gain insights and potentially secure a referral, emphasizing your expertise in agentic systems.
- Prepare for technical leadership interviews: Be ready to discuss complex AI evaluation challenges, your approach to operationalizing scientific goals, experience with building scalable platforms, and examples of technical mentorship and team development at Salesforce.
- Showcase your AI agent evaluation expertise: Articulate your deep knowledge of modern evaluation techniques like LLM-as-a-Judge and behavioral analysis, providing concrete examples of how you've used them to drive business outcomes in a production AI environment.
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