Senior AI Engineer, Agentforce Operations

Salesforce•San Francisco, CA

2d•Remote

About The Position

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Agentforce is the future of AI, and you are the future of Salesforce. Join an agile team with deep startup roots. We operate as a high-velocity 'startup-within-Salesforce,' following our recent acquisition. You’ll be working alongside the founding team and engineers in an organization offering the autonomy of a small team backed by the global scale and trust of Salesforce. You will have the unique opportunity to build a '0 to 1' product in one of the fastest growing verticals within Salesforce. Global supply chains still rely on slow, manual processes—email, spreadsheets, and fragmented data. Nowhere does modernizing them matter more than in the public sector, where the scale and stakes are highest. Missionforce Operations is reimagining the public sector supply chain with an AI-powered platform for designing, automating, and running end-to-end business processes, with seamless collaboration through familiar channels like email. For Salesforce, this represents a massive growth opportunity into public sector back office processes, with innovations that flow into the front office. Customers are clamoring for more, rapidly expanding their use cases as we enter an exhilarating growth phase.

Requirements

B.S. in Computer Science or equivalent with coursework in Artificial Intelligence (M.S. is a plus).
4+ years of industry experience in Software Engineering, with a focus in AI/ML.
Strong proficiency in multiple programming languages, such as Python, Go, Java, or C++.
Experience designing and operating production-grade distributed systems, APIs and data models.
Deep expertise in model evaluation, including designing custom benchmarks, automated evaluation suites, and production telemetry for monitoring model quality, drift, reliability, and safety.
Experience building production systems with LLM orchestration frameworks, along with the judgment to extend or move beyond those frameworks when reliability requirements demand it.
Demonstrated ability to lead complex technical initiatives, make sound architectural decisions, and deliver through ambiguity.
Proven ability to collaborate across engineering, product, customer-facing, and executive stakeholders to build alignment and drive decisions.
Experience mentoring engineers and raising the quality of technical execution across a team.
Excellent written and verbal communication skills.
Enthusiasm for learning and growing as a software engineer, and for helping your peers do the same.

Nice To Haves

Experience building products for regulated industries, particularly the public sector or environments with strong security, compliance, and data-sovereignty requirements.
Familiarity with developing for classified or limited-connectivity environments, including Department of Defense Impact Levels such as IL6.
Experience with enterprise-grade observability platforms, such as infrastructure as code, and cloud-native deployment practices.
Experience with containerization and orchestration technologies, such as Docker and Kubernetes.
Experience building AI products for supply chain, logistics, manufacturing, or operational workflows.
Contributions to open-source software, patents, publications, or other notable technical work.

Responsibilities

Partner with product and forward-deployed engineers to build reliable new features for our AI platform.
Lead development of intelligent agents that complete complex supply chain tasks with reliability and consistency.
Design and implement planning, orchestration, and evaluation systems that enable agents to execute multi-step workflows autonomously.
Make and drive architectural decisions for mission-critical, highly available systems that operate across varied and constrained deployment environments.
Establish engineering practices for model evaluation, AI safety, observability, reliability, and cost management.
Partner with Product Management and executive leadership to translate product vision into multi-year technical roadmaps.
Guide technical strategy for AI model deployment, safety constraints, reliability frameworks, and evaluation methodologies.
Drive the adoption of enterprise-grade observability, operational excellence, and cloud infrastructure practices.
Identify and mitigate technical risks related to security, compliance, scale, availability, and model behavior.
Help shape the organization by raising the engineering bar, contributing to hiring, and fostering a culture of continuous learning and high ownership.