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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