Software Engineer (MTS), Frontier Strike (EntSecTech)

Salesforce•Bellevue, WA

2d•$117,200 - $176,700

About The Position

Frontier Strike, part of Enterprise Security Technology (EntSecTech), builds and operates the Harness Orchestrator, an autonomous AI red-teaming platform that continuously tests our own identity and AI-integrated systems the way a real attacker would. This role builds the orchestration engine, policy gateways, and secrets-isolation systems that let the team safely point automated adversarial testing at production. AI serves as a core part of the development workflow here, pairing hands-on distributed-systems engineering with modern AI-assisted tooling to ship secure, production-grade code faster.

Requirements

2 - 4 years of professional software development experience.
Demonstrates proficiency in Go; experience with Python or Java is also acceptable.
Has experience with distributed systems, microservices, and Representational State Transfer (REST) or gRPC application programming interfaces (APIs).
Is familiar with Kubernetes, Docker, Helm, and Terraform in a cloud environment, AWS preferred.
Understands software security fundamentals: the OWASP Top 10, least privilege, and secrets management.
Shows strong problem-solving and debugging skills in distributed, containerized systems.
Works comfortably in a large-scale enterprise environment with production-safety constraints.
Takes a demonstrated, genuine AI-first approach to engineering, using AI to move faster, build fluency across the stack, and contribute well beyond a core specialty.
Has experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor) in development workflows.
Applies advanced prompt engineering skills, writing precise, structured prompts and cultivating the system context that makes AI outputs reliable, secure, and production-ready.
A Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent experience.

Nice To Haves

Experience with policy engines (e.g., 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 (HashiCorp Vault, cloud key management services (KMS), etc.).
Familiarity with sidecar proxy/gateway patterns and mutual Transport Layer Security (mTLS).
Experience with identity and access management (IAM), cybersecurity, or compliance frameworks (NIST, ISO, SOC 2).

Responsibilities

Design and extend Go microservices that orchestrate multi-container test suites (primary and utility containers, shared ephemeral volumes, lifecycle synchronization) so new attack and test techniques can be onboarded as repeatable, automated suites.
Build policy-based sidecar gateways that mediate every test suite's access to its target system, whether mocked, staging, or production, enforcing least-privilege and blast-radius limits as tests move up the risk tiers.
Implement pipeline stages that dedupe, suppress noise, and auto-escalate critical findings (injection, server-side request forgery (SSRF), privilege escalation, data exfiltration) so only real signal reaches the persistent findings store.
Design per-test-suite secrets isolation (dedicated vault, pod, and service account per suite) with automated credential rotation, so a compromised or misbehaving test suite can't reach another suite's credentials.
Deploy and operate containerized workloads on Kubernetes via Helm and Terraform on Amazon Web Services (AWS), at a scale where individual test runs can cost significantly more or less depending on suite complexity.
Integrate with internal large language model (LLM) gateway services to track token usage and cost per scan, and build throttling and authorization controls for expensive test executions.
Implement role-based access control (RBAC), network sandboxing, and geofencing so automated red-teaming can be safely extended from test environments to production.
Monitor and troubleshoot the platform's distributed components, ensuring findings-pipeline data integrity and proactively catching misconfigurations before they become production incidents.
Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow, pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
Contribute to building and maintaining shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
Critically evaluate code, whether human- or AI-generated, for correctness, quality, security, and performance.