Principal Software Engineer (Data Scientist, AI)

• The SDB AI team builds the foundation that makes agentic engineering on Salesforce Database safe, correct, and reusable across the org. Salesforce Database is the multi-tenant production database behind Salesforce’s cloud platform no general-purpose model knows. We encode that database-specific knowledge, prove that agents acting on it behave correctly, and take agentic work all the way to production
• You will own the design and hands-on implementation of the agentic AI foundation for database engineering and operations: full-stack, full-life-cycle agentic engineering and workflow automation, knowledge graphs and agentic memory, and unified agentic evaluation with data curation
• Build the agentic memory the agents reason over – the knowledge and context graphs that hold the database schema, runbooks, query templates and escalation-policy – and keep it accurate as the database evolves.
• Build closed-loop evaluation as infrastructure: local tracing, replay and golden-trace regression, PR-gating, and scoring against domain-grounded rubrics – proving not only that output is correct but that the execution path is.
• Advance the production agent from workflow-shaped to agent-shaped: a dynamic, causal planner and the specialized tools database workflows need beyond off-the-shelf MCP.
• Take agentic work all the way to production – through the platform, identity, and integration layers where the real bottleneck lives – and operate it at machine speed across hundreds of incidents a week with high-accuracy root-cause analysis, capacity planning, and early detection of anomalous events – with high-accuracy outcomes.
• Build the guardrails and telemetry that keep agents grounded: context-drift detection, activation instrumentation, and failure-mode analysis.
• Partner with database engineering teams to turn one team’s pattern into every team’s starting point – paved paths, deduplication, and quality gating that convert org-scale sprawl into one trustworthy library

Benefits

• Medical Care
• Life Insurance
• Retirement Savings
• Employee Assistance Programs
• With 9 standard holidays and four floating holidays, you get a total 13 paid days off each year- Judgment about what makes agents reliable at scale: grounding, evaluation, and guardrails against the failure modes – hallucination, context drift, wrong remediation – that cause incidents.
• Strong programming in Python and Java in a Unix/Linux environment; solid grasp of distributed systems.
• Background and experience in Data Science highly desirable
• Track record of shipping and operating production software at scale, with a bias for hands-on delivery over specification.
• Hands-on experience building and shipping full-stack, full-lifecycle agentic AI systems in production – LLM orchestration, tool and MCP integration, retrieval and knowledge grounding, and evaluation.
• Familiarity with the modern data and agent stack a plus: tracing and experiment tracking (e.g. MLflow), workflow orchestration (e.g. Airflow), distributed query engines (e.g. Trino), knowledge graphs, and model-agnostic gateways across frontier models.
• Experience building automation for root-cause analysis and self-remediation of production systems is a strong plus.
• Strong written and verbal communication; able to drive AI discussions across engineering teams and influence without authority.
• You should have 10+ years of professional experience. Experience with large-scale distributed databases or multi-tenant SaaS architectures is a strong plus