Software Engineer 1 - Data Platforms

About the Role

Enterprises of all sizes trust Abnormal AI’s cloud products to stop cybercrime. These AI-native, data-intensive SaaS applications depend on fast, reliable, and secure access to massive datasets. That’s where the Data Platform team comes in: designing and operating scalable storage systems (PostgreSQL, OpenSearch, Redis, RocksDB, DynamoDB), batch and stream data processing (Kafka, Spark), orchestration (Airflow, DBT), and more.

We build and maintain the core infrastructure that powers Abnormal’s most data-heavy workloads, providing scalable, reliable, and efficient data platforms and services to all of engineering and data science. We also create tools that make this infrastructure simple to operate and integrate, enabling teams to deliver faster with confidence.

We’re looking for a Software Engineer I who is eager to learn and ready to take on meaningful technical challenges. You’ll work alongside experienced engineers, contribute to ambitious projects, and grow your skills while helping to scale the systems that support Abnormal’s rapid growth.

What you will do

• Contribute to the design, development, and operation of core components of Abnormal’s data platform.
• Build tools and services that make it easy for other teams to adopt and scale data systems.
• Help automate infrastructure and operations to improve reliability, performance, and scalability.
• Apply cutting-edge GenAI techniques to build smarter developer and operator experiences.
• Collaborate across engineering teams to solve scalability and reliability challenges.
• Take ownership of projects, delivering features end-to-end with guidance from senior engineers.

What you bring

• 1+ years of professional experience in software engineering: backend, distributed systems, or infrastructure preferred.
• Solid programming skills in Python, Go, or a similar language (school, personal, or work experience all count).
• Strong software engineering fundamentals like data structures, clean code, testing, and debugging.
• Interest in learning about AWS, Databricks, and technologies in our stack (databases, streaming, orchestration).
• Curiosity, problem-solving skills, and a willingness to ask questions.
• Ability to work both independently and as part of a team, and strong communication skills.
• Nice to Have (but you can also learn here)
• Experience with one or more of: Kafka, Kinesis, Flink.
• Familiarity with cloud platforms (AWS, GCP, Azure).
• Exposure to large-scale or data-intensive systems.
• Why you’ll love it here
• You’ll be part of a high-impact team solving problems that matter.
• You’ll learn from and work with experienced engineers in a collaborative, supportive environment.
• You’ll get real opportunities for ownership and career growth early in your journey.
• You’ll help shape the future of our data platform at a fast-growing, AI-first company.
• Actual compensation will be determined based on several non-discriminatory factors including skills, experience, qualifications, and geographic location.
• In addition to base salary, this role may be eligible for bonus or incentive compensation, equity, and a comprehensive benefits package.

Base salary range: $122,400 — $176,000 USD

A note on AI in our 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 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.

Further, if your application is successful and Abnormal AI makes a conditional offer of employment, we will carry out pre-employment checks which must be successfully completed to progress to a final offer. All processes and pre-employment checks are in line with prevailing legislation and Abnormal AI's policies relevant to our security and privacy standards.

. If you would like more information on your EEO rights under the law, please click here.