About the Role

Enterprises of all sizes trust Abnormal’s AI-native security products to stop cybercrime and protect critical communications, identities, and infrastructure in the cloud. Our products are data- and systems-intensive, operating at high scale and low latency across multiple clouds and regions.

As a Software Engineer I on the Federated Intelligence Platform (FIP) team: you will design and maintain high-volume ingestion and low-latency services across global infrastructures, playing a vital role in upholding detection efficacy as the company expands its security footprint into identity and AI-agent protection. You’ll learn how to design and implement production-quality systems, work in an AI-native engineering environment, and contribute to services that are reliable, scalable, and security-critical.

FIP serves as the federated intelligence layer within the Abnormal Data Platform, managing the ingestion, storage, and retrieval of critical entity insights and threat indicators that drive core detection and the customer portal.

What You’ll Do

• Implement and ship well-scoped features and improvements in collaboration with more senior engineers, from design review through implementation, testing, and rollout.
• Own smaller tasks and components end-to-end, including clarifying requirements, breaking work into steps, writing code, and validating changes in test and production environments with support from the team.
• Collaborate on the reliability and performance of existing systems by fixing bugs, addressing simple bottlenecks, and contributing to refactors that improve readability, maintainability, and correctness.
• Participate in operational work appropriate to your level, such as helping debug issues, improving runbooks, and learning incident-handling practices before joining the formal on-call rotation.
• Work closely with partner teams and stakeholders (other engineering teams, Detection/ML, Product, Infra/Platform) to understand how your changes interact with upstream and downstream systems.
• Use AI tools as part of your development loop—for code suggestions, tests, documentation, and experiments—while learning how to validate AI-generated output and maintain engineering-quality standards.
• Contribute to documentation and knowledge sharing by writing clear comments, updating docs and runbooks, and sharing learnings from projects, code reviews, and incidents.
• Invest in your growth by seeking feedback, pairing with teammates, and taking on progressively larger and more ambiguous work as you gain experience.

Must Haves

• 0–2+ years of professional software engineering experience, or equivalent experience from internships, research, or significant personal projects, ideally in backend, infrastructure, full stack, or other production-oriented systems.
• Solid programming skills in at least one modern language used at Abnormal (e.g., Python, Go, TypeScript/JavaScript, or similar)
• Strong software engineering fundamentals: data structures, basic algorithms, writing clean and testable code, debugging, and working with version control (e.g., Git).
• Practical experience with relational or NoSQL storage solutions and schema design
• Competency in cloud infrastructure and container orchestration using AWS, Docker, or Kubernetes
• Foundational knowledge of high-scale event-driven architectures and message queuing
• Clear, concise communication skills: you ask good questions, can explain your thinking, and collaborate well in a remote, distributed team environment.
• A strong growth mindset and willingness to learn—you seek feedback, own mistakes, and are motivated to keep improving your craft and impact.

Nice to Have Skills

• Experience with distributed systems, high-throughput pipelines, or large-scale data stores (e.g., PostgreSQL, DynamoDB, Redis, RocksDB, Kafka, Spark, OpenSearch/Elasticsearch)
• Familiarity with Airflow or equivalent workflow orchestration and data pipeline management tools
• Background in production observability using Grafana or Prometheus and experience with on-call rotations
• Proficiency and comfort utilizing AI-augmented development workflows and engineering toolsets
• Background or coursework in security, threat detection, or large-scale messaging systems, particularly systems processing significant volumes of data or requests.
• Exposure to containerization and orchestration (Docker, Kubernetes) and infrastructure-as-code tooling.

Why You’ll Love It Here

• You’ll solve hard, meaningful problems at the intersection of AI, security, and large-scale distributed systems.
• You’ll work with smart, kind, and ambitious teammates who care deeply about craftsmanship, learning, and helping each other grow.
• You’ll get real ownership and autonomy over important parts of our systems and roadmap, with clear opportunities to grow toward Senior and Staff roles over time.
• You’ll be part of an AI-native R&D organization, with strong investment in tools, workflows, and training to help engineers use AI to move faster while raising the quality bar.

#LI-AD2