Founding AI Infrastructure Engineer

Harden; San Francisco (CA); Hybrid; USD 150,000 - 250,000; Full time

Benefits offered by this job

Equity

Job summary

Harden is seeking a founding AI Infrastructure Engineer to own the foundations for AI-powered workflows, from prototype to production. You will design distributed systems, build scalable Kubernetes-based infrastructure, and shape security-sensitive runtimes in customer environments.

You’ll work across data/model pipelines, observability, and API surfaces, delivering fast, reliable, and auditable platforms that integrate with customers' CI/CD, SIEM, IAM, and cloud ecosystems in a high-ownership

Qualifications

• Required: strong production distributed systems experience.

Responsibilities

• Design and build production services for AI/ML workloads with clear SLOs.
• Own Kubernetes deployments end-to-end: rollout, autoscaling, networking.

Skills

Distributed systems

Kubernetes

Python

Go/Rust/C++

AWS/GCP/Azure

Performance engineering

API design

Security-aware infra

Tools

Kafka

Datadog

CI/CD tooling

Terraform

Job description

# Founding AI Infrastructure EngineerRemote (US) or Hybrid (SF Bay Area)Full-time$150,000–$250,000 + equity## About UsHarden is the enterprise security and governance platform for AI-built apps. Every AI-generated application ships with the same gaps — hardcoded secrets, missing auth, uncontrolled egress, no audit trail. Harden closes them automatically: build-time SAST and SCA scanning with auto-remediation, a one-page approval report that gates CI/CD, and runtime enforcement that covers egress controls, prompt-injection defense, AI cost caps, and tamper-evident audit logs — all deployed inside the customer's own cloud.We're a venture-backed startup based in the SF Bay Area. The founding team combines AI/ML research leadership at Google DeepMind and Amazon with product leadership at Verkada and AWS. Enterprise customers are already in production and we're bringing on exceptional engineers to build the infrastructure that makes enterprise AI adoption safe and scalable.## About the RoleYou'll own the engineering foundations for taking AI-powered workflows (including agentic systems) from prototype to reliable production. At Harden, that means building the infrastructure that analyzes, transforms, and governs AI-built applications in customer-controlled environments. This is a high-ownership, hands-on role spanning distributed systems, ML systems, Kubernetes/cloud infrastructure, performance engineering, and enterprise-ready platform design.You'll build core platform capabilities (execution/runtime, data/model pipelines, APIs, observability, scaling) and ensure the system is fast, resilient, secure, and easy to integrate into a customer's existing stack, including VPCs, CI/CD pipelines, SIEM, identity, ticketing, and compliance workflows.## What You'll Do* •Design and build production services for AI/ML workloads with clear SLOs (latency, throughput, availability), including synchronous request paths and safe fallbacks for Harden's scanning, remediation, and runtime enforcement systems* •Build a sandboxed execution runtime for running customer-provided or semi-trusted logic safely at scale (isolation boundaries, cold-start mitigation, warm pools, resource governance), including generated-code analysis environments* •Build and operate large-scale data + embedding/model pipelines (batch processing, feature generation, training data preparation, serving-friendly formats) for findings, dependency graphs, approval reports, telemetry, and audit trails* •Architect event-driven systems using Kafka-style streaming for ingestion, replayability, and decoupling offline pipelines from latency-sensitive online services* •Own Kubernetes deployments end-to-end: rollouts (blue/green, rolling), autoscaling, networking (ingress, gateways, service-to-service), resource tuning, and on-call grade debugging (OOM, crash loops, throttling)* •Build platform-grade observability: metrics, logs, traces, dashboards, alerting, and incident runbooks; instrument application-level profiling for memory/GC and performance regressions* •Implement multi-tenant API controls: API key management, quotas, rate limiting (token/leaky bucket), request scheduling/fairness, backpressure, and retry strategies with jitter* •Drive performance optimizations across the stack (parallelization, serialization/I/O bottlenecks, caching/batching), and rewrite hotspots when needed* •Build enterprise-ready integrations-first product surfaces (fit into Datadog/ServiceNow/identity/logging workflows instead of "one more dashboard"), including CI/CD, SIEM, and customer-cloud workflows* •Partner with product and customers to translate ambiguous requirements into robust, developer-friendly platform primitives for secure AI-built application deployment## What We're Looking For* •Strong experience building production distributed systems (service design, reliability patterns, scaling strategies, failure modes)* •Proven track record in performance engineering (profiling, concurrency/parallelism, bottleneck elimination, cost/perf tradeoffs)* •Deep hands-on experience with Kubernetes in production (deployments, networking, autoscaling, debugging, observability)* •Solid cloud fundamentals (AWS/GCP/Azure): compute lifecycle automation, networking, IAM basics, cost controls, and operational tooling* •Experience designing low-latency APIs and making pragmatic tradeoffs between latency, availability, and consistency* •Familiarity with streaming/event systems (Kafka/event sourcing) and building pipelines that support replay and auditability* •Strong programming skills (Python plus at least one systems language like Go/Rust/C++ is a plus)* •Comfortable operating in a fast-moving startup environment: clear communication, high ownership, and good engineering judgment* •4+ years experience in backend/platform/infra roles (or equivalent depth)* •Comfort with security-sensitive infrastructure such as sandboxed execution, tenant isolation, secrets handling, or customer-controlled cloud deployments## Nice to Have* •Experience building sandboxed runtimes or isolation layers (microVMs, gVisor, containers, secure execution boundaries)* •Built large-scale embedding/recommendation or retrieval pipelines and served them in production* •Experience with multi-tenant platform concerns: noisy neighbor mitigation, quota enforcement, fairness scheduling, per-tenant observability* •Strong opinions on enterprise integrations and "platform adoption" mechanics (connectors-first, workflow-native design)* •Experience implementing safe progressive delivery for ML-backed systems (shadowing, canarying, rollback automation, regression gating)* •Experience with infrastructure-as-code, progressive delivery, CI/CD, and production readiness practices from scratch* •Security platform, DevSecOps, or enterprise SaaS infrastructure experience