AI Systems Engineer

Menlo Security is the leader in Browser Security for human and agentic workforces. Our mission is to enable humans and agents to connect, communicate, and collaborate securely, without compromise. The Menlo Browser Security Platform protects organizations from cyberattacks by stopping threats across the web, documents, and email before they reach the user. With Menlo Agent Runtime Security (MARS), that protection now extends to the AI agents working alongside every employee. Menlo Security is trusted by major global businesses, including Fortune 500 companies and government agencies, to protect their most valuable asset, their data, and is backed by top-tier investors. We are seeking an AI Systems Infrastructure Engineer (IC4) to lead the platform and backend architecture powering our real-time security detection services. In this role, you will build and scale low-latency serving infrastructure for Small Language Models (SLM), Prompt Injection (PI) engines, and Content / Intent Classification platforms running across GPU and cloud clusters. We are looking for a strong systems engineer who understands production backend services, microservices, container management, and high-throughput inference optimization. While you will work closely with ML researchers, your primary focus will be engineering robust backend services, GPU replica scaling, guardrails, and automated evaluation infrastructure. Outcomes & KPIs Key Outcome(s) Owned: • Sub-hundred-millisecond SLM and detection service execution speeds across high-volume tenant workloads. • Highly resilient, auto-scaling GPU inference cluster infrastructure with automated CI/CD and regression gating. Success Metrics / KPIs : • P95 and P99 detection latency for prompt injection (PI) and classification services. • GPU pod readiness time and cluster auto-scaling efficiency under burst traffic. • Service availability and rate-limiting guardrail enforcement per tenant. • Automated MLOps/LLMops pipeline throughput and deployment turnaround time. What You'll Do • Architect, build, and optimize production serving pipelines for Small Language Models (SLMs) and traditional ML models using model quantization, batching, and GPU acceleration. • Own and scale backend microservices (e.g., sds-service-prompt-injection, sds-service-ddr) delivering real-time threat detection. • Build consumption-based monitoring, tenant rate-limiting, and runtime guardrail infrastructure. • Manage and optimize GPU cluster replicas (Kubernetes / Pod infrastructure) to minimize cold-start times and manage infrastructure costs. • Build continuous evaluation, automated retraining pipelines, model CI/CD, and automated regression gates to test model detection quality against live benchmarks. • Refactor legacy service boundaries, service-to-service auth flows, and backend database configurations. Requirements: • 5+ years of software engineering experience building distributed backend systems or high-throughput microservices. • Proven track record in model serving and inference optimization (e.g., TensorRT, vLLM, ONNX Runtime, Triton Inference Server). • Strong programming experience in Python, Go, or C++. • Hands-on expertise with Kubernetes, container orchestration, GPU scheduling, and infrastructure auto-scaling. • Solid understanding of CI/CD pipelines, MLOps/LLMops principles, and cloud infrastructure (AWS/GCP). Preferred / Nice to Have : • Experience building consumption-based rate limiting or multi-tenant guardrail architectures. • Familiarity with database refactoring, auth protocols, and backend technical debt remediation. • Background in cybersecurity, threat detection engines, or DLP/PII filtering platforms. • Experience benchmarking runtime vs. accuracy trade-offs between local SLMs and clou