Software Engineering MTS

Salesforce•Bellevue, WA

About The Position

Salesforce is seeking a Member of Technical Staff (MTS) Engineer for the Data Security Fabric team. This role involves designing and building a secure, cloud-native, and highly scalable Data Platform to measure, mitigate, and reduce enterprise risk. The platform will unify disparate security data to provide actionable insights and automate remediation. The engineer will be responsible for designing and implementing a robust data platform that collects and processes 600 TB of data daily in real-time streaming, including application logs, asset information, vulnerability data, user identity and access data, and security signals from various Salesforce platforms and environments like AWS and GCP. The role will leverage cutting-edge technologies to build a next-generation security data platform and a vendor-agnostic core security system. AI and machine learning are key capabilities, with responsibilities including designing LLM- and ML-driven workflows for security signal enrichment, anomaly detection, risk scoring, and automated triage, as well as integrating agentic patterns into remediation and investigation flows. The role also involves establishing data governance and quality frameworks. This is an opportunity for an engineer passionate about distributed systems, big data processing, AI/ML, and security to shape the future of security at Salesforce, driving innovation, improving risk management, and enabling automation.

Requirements

2+ years of industry experience for MTS, including 2+ years in SaaS, PaaS, or IaaS software development
Bachelor's or Master's degree in Computer Science or Engineering (or equivalent experience)
Distributed systems and data engineering expertise, including: High-performance, high-availability (99.99%), highly fault-tolerant systems
Large-scale infrastructure systems
Docker-based development, especially with EKS
Configuration management systems, including Infrastructure-as-Code (IAC), Terraform, Puppet
Data technologies: Apache Spark & Kafka, Hadoop, SQL/NoSQL
Programming proficiency in object-oriented and multi-threaded development using at least one of: Python, Golang, Java/Scala
Demonstrated expertise applying systems patterns (e.g., client-server, N-tier, primary/secondary, MVC) and API construction (e.g., Swagger, OpenAPI)
Experience developing and managing software on Linux (e.g., CentOS or RHEL)
Strong fundamentals in security concepts: authentication/authorization frameworks (e.g., SSO, SAML, OAuth), secure transport (e.g., TLS), identity management (e.g., certificates, PKI)
Hands-on experience integrating LLMs or ML models into production systems, including at least two of: RAG pipelines, agent/tool-use frameworks (e.g., MCP, LangGraph), prompt engineering, evals/observability for LLM apps, fine-tuning, or embeddings/vector stores
Demonstrated fluency with agentic coding tools (Claude Code, Cursor, Copilot, or equivalent) as a daily driver, with the ability to guide the team on effective and safe usage patterns
Excellent oral and written communication skills
Ability to manage multiple projects, meet deadlines, and adapt to shifting priorities
Strong collaborator who values team success alongside individual contributions
Ability to translate strategic or operational goals into technical and tactical requirements and architecture design

Nice To Haves

Experience applying AI/ML to security use cases — e.g., anomaly detection, alert triage, vulnerability prioritization, or SOC automation
Experience with MCP (Model Context Protocol), tool-use frameworks, or building agentic workflows on top of security data
Familiarity with LLM cost/latency optimization, guardrails, prompt-injection defense, and evaluation frameworks
Experience with vector databases and embedding-based retrieval over structured/unstructured security data

Responsibilities

Learn and adapt to Salesforce's security strategies, goals, objectives, and capabilities to improve security posture.
Lead the design and architecture of a highly scalable and secure data platform that ingests and processes diverse security data sources across 10+ Salesforce platforms and 15+ external environments.
Build and optimize data pipelines to collect, store, and analyze security signals from tools and platforms (e.g., vulnerability scanners, asset management systems, identity and access control systems) running across multiple environments (AWS, GCP, Salesforce, CRM, vendor systems, etc.).
Design and integrate AI/ML and LLM-driven capabilities into the platform — including RAG over security data, agentic triage/remediation workflows, anomaly detection, and risk scoring — with strong attention to evals, guardrails, and cost/latency tradeoffs.
Work closely with cross-functional teams (e.g., Engineering, Security Operations, Risk Management, Product Security, and Data Science) to align the data platform with business and security goals.
Participate in Agile development, including daily syncs.
Support the team's engineering excellence through code reviews and mentoring for team members at all levels.
Provide technical leadership to a team of engineers, driving best practices in software development, security, AI-assisted development, and cloud-native architecture.
Mentor engineers at earlier career stages, fostering a culture of continuous learning, innovation, and excellence.
Champion effective use of AI-assisted development tools (e.g., Claude Code, Cursor, Copilot) across the team — establishing patterns for agent-driven workflows, code review, and productivity while maintaining code quality and security.
Own and deliver initiatives that add new features to meet growing product demands.
Adapt quickly to changing requirements, priorities, and strategies.
Advocate for security and secure practices throughout Salesforce, including secure AI/agent design (prompt injection defenses, least-privilege tool access, data handling for LLM contexts).