Abnormal AI 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
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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
LI-FS1
AI and our hiring process Abnormal AI uses AI-assisted tools to help our recruiting team prepare for candidate interviews. These tools analyze resume content and role requirements to suggest interview questions and identify areas for the interviewer to explore. They do not make hiring decisions or screen candidates automatically. Every decision about a candidacy is made by a person.
Abnormal
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