Staff Software Engineer - Platform & Infrastructure

Enterprises of all sizes trust Abnormal Security’s cloud products to stop cybercrime—and these products are only as powerful as the platform they run on. The Platform Infrastructure team builds and operates the core systems that make Abnormal’s AI-driven detection and prevention possible: delivering reliability, scalability, and security at cloud scale.

We’re looking for a Staff Software Engineer to lead foundational efforts across multiple areas of Platform Infrastructure. In this role, you’ll guide a high-performing team, shape the roadmap for a true self-service infrastructure platform, and drive ambitious technical projects that use AI to automate and elevate how we build and operate our systems.

The ideal candidate:

• Tackles complex, ambiguous problems and turns them into actionable plans.
• Leads by example and dives deep when needed.
• Embodies our VOICE values and builds software that delights customers.
• Earns trust across Engineering, Product, and Design through thoughtful collaboration.

Team mission: Build and evolve the core infrastructure—compute, orchestration, and data platform—that powers Abnormal’s AI/ML products at scale. We treat platforms as products: usable, reliable, secure, and cost-efficient.

• Shape the core areas of Platform Infrastructure such as compute (EC2/EKS, autoscaling, container runtime) and orchestration (Kubernetes, workload APIs, multi-cluster, policy/quotas), as well as data platform (streaming, batch, durable storage, data tooling)—with demonstrated depth in at least two of these.
• Design and drive platform architecture & roadmap to support Abnormal’s expanding AI/ML portfolio—scaling seamlessly across services, tenants, and regions.
• Partner deeply with product & ML workflows to make pragmatic trade-offs, accelerating our shift to a platform-first operating model and enabling self-service.
• Raise the bar on operational excellence (SLOs, availability, performance, incident response, change management, on-call hygiene) and help teams consistently meet it.
• Act as the team’s technical lead: define quarterly roadmaps, de-risk delivery, mentor engineers, and land high-leverage, cross-team initiatives.
• Champion AI-native software development, guiding teams on architecture, data gravity, feature stores, model/service interfaces, and evaluation pipelines.
• Own cost-conscious engineering, optimizing design and operations to balance performance, reliability, and spend (capacity planning, right-sizing, caching, storage tiers).
• Instill strong platform product practices: crisp APIs, great docs, clear SLAs/SLOs, telemetry by default, and paved paths that increase developer velocity.

• Proven experience building and scaling data-intensive, distributed backend systems in high-growth environments.
• 5+ years as a Senior/Staff engineer building platforms, tools, or infrastructure that materially increase engineering velocity and reliability.
• A strong track record as a change agent—reshaping infra strategy and shipping impactful, self-service platform offerings in startup settings.
  
• Depth in at least two of the following three areas:
1. Compute (e.g., EC2, autoscaling, container runtimes, networking, security hardening)
2. Orchestration (e.g., Kubernetes/EKS, controllers/operators, scheduling, policies, multi-cluster)
3. Data Platform (e.g., Kafka/Kinesis/SQS; Spark/Databricks/DBT/Airflow; S3; PostgreSQL/MySQL; DynamoDB/RocksDB/Redis/OpenSearch; data governance/quality/lineage)
   
• Hands-on with our stack (or equivalent): Python, Golang, Terraform/Terragrunt, PostgreSQL, Kafka, Redis, OpenSearch, AWS, Kubernetes.
  Strong IaC, observability, and SRE fundamentals (SLOs, error budgets, incident management, postmortems, capacity planning).
  

• Experience building multi-tenant or regulated (e.g., FedRAMP-like) platforms, isolation boundaries, and guardrails.
• Background with feature stores, offline/online consistency, model serving, and evaluation/feedback loops.
• Prior leadership of cross-org migrations (e.g., to Kubernetes, event-driven architectures, or a unified data platform).

• Product mindset: platform as a product with clear APIs, docs, SLAs, and adoption metrics.
• Automation first: paved paths and golden configs over bespoke snowflakes.
• Measured outcomes: reliability, latency, cost, and developer experience over vanity metrics.

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