Principal Software Engineer / Data Scientist, AI

Salesforce

Confirmed live yesterday High trust

Hybrid

Quick summary

Work type: Hybrid
Location: San Francisco, CA
Employment: Full-time
Posted: 53 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $219k

$127k $291k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $188,350–$248,815.

Based on 240 similar postings.

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About Salesforce

Salesforce is the world''s leading customer relationship management (CRM) platform, offering cloud-based software for sales, service, marketing, analytics, and application development. Industry: Enterprise Software & Cloud Computing

Salesforce currently has 221 open roles on FindRole.

Listed pay typically runs $148,500–$260,100 across 125 roles with salary data.

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At a glance

TL;DR · Principal Software Engineer / Data Scientist, AI

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The Principal Software Engineer / Data Scientist, AI joins the SDB AI team to build the foundation for agentic engineering on the Salesforce Database. This role involves owning the design and hands-on implementation of full-stack, full-life-cycle agentic engineering and workflow automation. Key responsibilities include building agentic memory using knowledge and context graphs, developing closed-loop evaluation infrastructure with local tracing and golden-trace regression, and creating guardrails for context-drift detection. The candidate will utilize Python and Java in a Unix/Linux environment, leveraging distributed systems, MLflow, Airflow, Trino, and knowledge graphs. The work addresses the technical challenge of making agents safe, correct, and reusable for database operations, specifically focusing on high-accuracy root-cause analysis, capacity planning, and early detection of anomalous events within a multi-tenant production database.

Skills

Python Java LLM Orchestration MLflow Airflow Trino Knowledge Graphs Distributed Systems Unix Linux Data Science SaaS Architecture MCP Telemetry Root-Cause Analysis

What you'll do

Design and implement full-stack, full-lifecycle agentic AI systems for database engineering and operations.
Build and maintain knowledge and context graphs for agentic memory including schemas and runbooks.
Develop closed-loop evaluation infrastructure for local tracing, replay, and regression testing.
Create dynamic causal planners and specialized tools for database workflows.
Deploy and operate agentic work across platform, identity, and integration layers at machine speed.
Build guardrails and telemetry for context-drift detection and failure-mode analysis.
Partner with engineering teams to create paved paths and unified libraries for org-scale patterns.

What we're looking for

You must have 10+ years of professional experience.
You must have hands-on experience building and shipping full-stack, full-lifecycle agentic AI systems in production.
You must have strong programming skills in Python and Java within a Unix/Linux environment.
You must have a solid grasp of distributed systems.
You must have a track record of shipping and operating production software at scale.
You must have strong written and verbal communication skills to drive AI discussions across engineering teams.
Experience with large-scale distributed databases or multi-tenant SaaS architectures is a strong plus.
Experience building automation for root-cause analysis and self-remediation of production systems is a strong plus (preferred).

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