Software Engineer (MTS), Frontier Strike (EntSecTech)

Salesforce•Bellevue, WA

5d•$117,200 - $176,700

About The Position

Salesforce’s Frontier Strike team, part of Enterprise Security Technology (EntSecTech), builds and operates the Harness Orchestrator, an autonomous AI red-teaming platform that continuously tests Salesforce identity and AI-integrated systems. The Software Engineer builds the orchestration engine, policy gateways, and secrets-isolation systems that enable safe automated adversarial testing of systems in test, staging, and production environments. The role combines distributed-systems engineering with AI-assisted development practices to deliver secure, production-grade software. The engineer develops Go microservices and containerized workloads, builds controls for access, authorization, secrets, and network safety, and operates platform components in Kubernetes on AWS. The position also supports findings pipelines, monitoring, troubleshooting, and the use of internal LLM services. AI is a core part of the development workflow, including critically evaluating human- and AI-generated code and maintaining shared system context that helps AI produce reliable results.

Requirements

2–4 years of professional software development experience.
Proficiency in Go; experience with Python or Java is also acceptable.
Experience with distributed systems, microservices, and REST or gRPC APIs.
Familiarity with Kubernetes, Docker, Helm, and Terraform in a cloud environment; AWS is preferred.
Understanding of software security fundamentals, including the OWASP Top 10, least privilege, and secrets management.
Strong problem-solving and debugging skills in distributed, containerized systems.
Comfort working in a large-scale enterprise environment with production-safety constraints.
A demonstrated AI-first approach to engineering, using AI to work faster, build fluency across the stack, and contribute beyond a core specialty.
Experience using AI tools such as Claude Code, GitHub Copilot, Codex, or Cursor in development workflows.
Advanced prompt-engineering skills, including writing precise, structured prompts and cultivating system context for reliable, secure, production-ready AI outputs.
Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent experience.

Nice To Haves

Experience with policy engines such as Open Policy Agent (OPA) or other fine-grained authorization frameworks.
Exposure to AI/LLM security, adversarial testing, or red-teaming.
Experience with secrets-vault technologies such as HashiCorp Vault or cloud key management services.
Familiarity with sidecar proxy/gateway patterns and mutual TLS (mTLS).
Experience with identity and access management, cybersecurity, or compliance frameworks such as NIST, ISO, or SOC 2.

Responsibilities

Design and extend Go microservices to orchestrate multi-container test suites, including container lifecycle synchronization and shared ephemeral volumes.
Build policy-based sidecar gateways that mediate each test suite’s access to target systems and enforce least privilege and blast-radius limits.
Implement pipeline stages to deduplicate findings, suppress noise, and automatically escalate critical security findings.
Design per-test-suite secrets isolation using dedicated vaults, pods, and service accounts, with automated credential rotation.
Deploy and operate containerized workloads on Kubernetes using Helm and Terraform on AWS.
Integrate with internal LLM gateway services to track token usage and cost per scan, and implement throttling and authorization controls for costly test executions.
Implement role-based access control, network sandboxing, and geofencing to safely extend automated red-teaming to production environments.
Monitor and troubleshoot distributed platform components, ensure findings-pipeline data integrity, and proactively detect misconfigurations.
Build and ship high-quality, production-grade software using modern engineering practices and AI-assisted development tools.
Design and orchestrate systems in which AI agents integrate into human workflows.
Contribute to shared system context documenting system designs, constraints, and standards for reliable AI operation.
Evaluate human- and AI-generated code for correctness, quality, security, and performance.