About the Role

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.

What you will do

• 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.

Must haves

• 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).
  
  

Nice to haves

• 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).

How we work

• 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.

#LI-ML1

About the Role

Abnormal Security is looking for an experienced and driven Platform & Infra software engineer to join the PI team. Join us and help build the platforms that power Abnormal's growth

• Observability Platform - Own and evolve the monitoring, metrics, and alerting infrastructure that every engineering team at Abnormal depends on. You'll work across the Prometheus, Chronosphere, and Grafana stack to ensure engineers can see what their systems are doing in real time — building dashboards, managing metric pipelines at scale, operating the PagerDuty alerting pipeline, and driving cost-efficient observability across all production environments (US, EU, and GovCloud).

  Your Impact

• Own the observability stack (Prometheus, Chronosphere, Grafana, PagerDuty) that every team relies on to detect, diagnose, and resolve production issues — when you make it better, every engineer at Abnormal gets faster.
• Design platforms and developer tooling that remove friction — reducing deployment times, simplifying pipeline authoring, and letting product teams focus on building rather than firefighting.
• Drive SLAs and SLOs for critical shared infrastructure ensuring the systems behind our products are resilient and cost-efficient.
•  Your architectural decisions on alerting pipelines and cross-environment deployments will define what products we can build and how quickly we deliver them to customers.

What you will do 

• Work with the Tech Lead, Engineering Manager, and Product Manager to design, develop, and deliver key platform features — from technical design docs through production rollout
• Own features end-to-end: scoping, implementation, testing, deployment, and post-launch monitoring across multiple environments (US, EU, GovCloud)
• Take ownership of 1-3 key services within Observability (Prometheus, Chronosphere, Grafana, PagerDuty pipeline) or Data Infra (Airflow, Spark) and be accountable for their reliability, performance, and evolution
• Participate in on-call rotations — triage, diagnose, and resolve production issues independently, building deep operational knowledge of the systems you own
• Improve system resilience by converting runbooks into automated solutions, refining SLAs/SLOs, and proactively identifying performance bottlenecks and failure modes
• Assume ownership of the reliability of everything you build, including comprehensive unit tests, integration testing, and observability instrumentation
• Build platforms, tooling, and APIs that make it easier for other engineering teams to ship — whether that's faster pipeline deployments, better dashboards, or simpler alerting configuration
• Partner with internal customers (product and engineering teams) to understand their needs and translate them into scalable platform capabilities
• Communicate effectively in an async-first, distributed environment — proactively providing updates, discussing challenges, and proposing solutions without prompting
• Mentor junior engineers on the team, helping them ramp up on service operations and development practices
• Raise the bar of engineering excellence through code reviews, knowledge sharing, design discussions, and contributing to team best practices

Must Haves 

• Backend Engineering & Distributed Systems (4+ years)
• 4+ years of hands-on backend engineering experience designing, building, and operating production-grade distributed systems
• Strong proficiency in Python — the primary language for Airflow DAGs, platform services, and automation tooling
• Working proficiency in Golang — used for high-performance infrastructure components, metric pipelines, and platform services
• Experience building systems that process data at scale — whether metric ingestion pipelines, stream/batch processing, or high-throughput API services
• Demonstrated experience owning a service or platform end-to-end — from technical design through production deployment, monitoring, and iteration
• Comfortable balancing feature development with operational responsibilities: you've shipped features and kept them running reliably at scale
• Experience writing technical design documents that articulate trade-offs, propose solutions, and get buy-in from peers and tech leads
• Track record of breaking down ambiguous problems into concrete, deliverable milestones
• Experience with fault tolerance patterns — retries, circuit breakers, graceful degradation, backpressure — and knowing when to apply each
• Proven incident response capability: you've been on-call, diagnosed production issues under pressure, and driven them to resolution
• Strong testing discipline — unit tests, integration tests, and an understanding of what to test and how to keep test suites maintainable
• Ability to design systems with a forward-looking perspective — thinking about how your architecture handles 10x growth, multi-region deployment, and evolving requirements
• Ability to contribute to and influence cross-team technical direction — you're not just implementing specs, you're shaping the solution
• Async-first communication excellence — strong written communication skills for design docs, Slack discussions, PR reviews, and status updates across time zones
• Proactive communicator — you surface blockers early, share con
• Solid understanding of monitoring, alerting, and observability principles — you've instrumented services, set up dashboards, defined SLIs/SLOs, or triaged production incidents using metrics and logs

Nice to Have 

• Hands-on experience with Prometheus — PromQL queries, recording rules, alerting rules, relabeling configs, and understanding metric cardinality challenges at scale
• Experience with Grafana — building dashboards, templating, managing datasources, and creating meaningful visualizations for operational and business metrics
• Familiarity with commercial observability platforms like Chronosphere, Datadog, New Relic, or Honeycomb — understanding trade-offs between self-hosted and managed solutions
• Experience designing or operating an alerting pipeline — PagerDuty, OpsGenie, or similar — including alert routing, escalation policies, and reducing noise/alert fatigue

Cloud Infrastructure & Kubernetes

• Familiarity with AWS services — EC2, ECS, EKS, S3, RDS, IAM, CloudWatch, Lambda, SQS/SNS — and understanding how to architect cost-effective, secure cloud infrastructure
• Experience with Kubernetes (K8s) — deploying and operating workloads, understanding pods/services/deployments, Helm charts, and debugging cluster-level issues
• Exposure to Infrastructure-as-Code tools — Terraform, Pulumi, or CloudFormation — and understanding the value of declarative infrastructure management
• Experience with CI/CD pipelines — GitHub Actions, Jenkins, or similar — and optimizing build/deploy times for platform services

Programming & Framework

• Experience with Django or similar Python web frameworks — building APIs, managing migrations, and understanding ORM performance characteristics
• Familiarity with gRPC or protobuf for inter-service communication in a microservices architecture

Technical Leadership & Platform Thinking

• Experience leading a small team (2-4 engineers) to build a feature or component from scratch — scoping, task breakdown, code reviews, and delivery management
• Experience building internal developer platforms or tooling — CLIs, SDKs, self-service portals, or automation that improved developer productivity
• Track record of reducing operational toil — automating runbooks