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Salary: USD 179,800 - 258,500 / annual
Abnormal AI is seeking a Senior Software Engineer to join the Detection Team, which builds advanced technology for identifying and stopping email and cloud-based attacks. In this role, you will architect, design, build, deploy, and maintain model serving infrastructure that supports a world-class detection engine. You will own projects that scale model serving and data processing services to handle 10x current traffic, build platforms for fighting AI-generated attacks, and own real-time and near real-time streaming pipelines and online feature serving services. You will also contribute to building Abnormal's ML training platform to improve machine learning engineer velocity and model precision/recall.
Key responsibilities include collaborating closely with machine learning engineers and data science teams by translating feedback into strategy and executing on it, and coaching and mentoring junior engineers through 1-on-1s, pair programming, code reviews, and design reviews.
The ideal candidate brings a first-principles approach to building scalable, customer-centric solutions; a drive to solve meaningful and pragmatic problems; an ownership and impact-oriented outlook; and the ability to iterate in real-time, solving novel problems quickly and autonomously.
REQUIREMENTS:
- 5+ years of experience as a Software Engineer or similar role, with hands-on experience building ML-engineering-focused solutions
- Experience maintaining large-scale distributed systems on cloud platforms (AWS, GCP, or Azure) with strong grasp of cloud-based engineering best practices
- Experience maintaining real-time and near real-time data pipelines or streaming services at high scale
- Proven ability to collaborate effectively with cross-functional teams (data scientists, ML engineers, product managers, stakeholders); can translate requirements into actionable technical tasks, communicate progress clearly, and adapt to feedback
- Excellent problem-solving skills and ability to work independently in fast-paced environments; can break down complex challenges into manageable steps and iterate on solutions while balancing immediate needs with long-term scalability
- Familiarity with machine learning workflows and requirements to support ML engineer teams, including feature development and serving at 50K+ QPS, offline/online equivalency, and large batch jobs for data gathering and training of tree and deep learning models
- Experience with streaming data architectures and real-time processing
- Knowledge of security and compliance frameworks as they relate to data engineering and data privacy
