The job, in the employer's words
About the Role
Abnormal AI is looking for a Senior Software Engineer to join the Detection Team. The Detection Division is focused on building the world’s most advanced technology for identifying and stopping email and cloud-based attacks that were previously undetectable and helping make the world a safer place. As a Senior Software Engineer building systems for Detection’s Signals and Serving Team, you will make feature development at Abnormal fast, responsive, stable, and confident for our ML and Data Science team.
The ideal candidate would have the following qualities:
- A first principles approach to building scalable, customer-centric solutions
- A drive to solve meaningful & pragmatic problems for real-world people
- An ownership and impact-oriented outlook on your efforts and growth
- An ability to iterate in real-time-solving novel problems, quickly and autonomously
An ability to iterate in real-time - solving novel problems, quickly and autonomously
What you will do
- Architect, design, build, deploy, and maintain Model Serving infrastructure that supports a world-class Detection Engine
- Own projects that scale our model serving and data processing services to handle 10x the traffic we serve today
- Build the platform for fighting against rapidly generated AI attacks
- Own real-time, near real-time streaming pipelines, and online feature serving services
- Build Abnormal’s ML Training platform, improving MLE velocity and product precision and recall
- Collaborate closely with MLE and Data Science teams by distilling feedback, correlating it to strategy, and executing
- Coach and mentor junior engineers via 1on1s, pair programming, high-quality code reviews, and design reviews
Must Haves
- 5+ years of experience as a Software Engineer or in a similar role, with hands-on experience in building ML-engineering focused solutions.
- Experience maintaining large-scale distributed systems on cloud platforms such as AWS, GCP, or Azure, including a strong grasp of cloud-based engineering best practices.
- Experience with maintaining real-time and near real-time data pipelines or streaming services at high scale
- Proven ability to collaborate effectively with cross-functional teams, including data scientists, machine learning engineers, product managers, and other stakeholders. You can translate requirements into actionable technical tasks, communicate progress clearly, and adapt to feedback.
- Excellent problem-solving skills and the ability to work independently in a fast-paced environment. You can break down complex challenges into manageable steps and iterate on solutions, balancing immediate needs with long-term scalability.
- Familiarity with machine learning workflows and requirements to support MLE teams effectively. This includes feature development and serving at 50K+ QPS, offline/online equivalency, 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.
#LI-PP1
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.
Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please click here. If you would like more information on your EEO rights under the law, please click here.
Requirements mentioned
- 5+ years of experience as a Software Engineer or in a similar role, with hands-on experience in building ML-engineering focused solutions.
- Experience maintaining large-scale distributed systems on cloud platforms such as AWS, GCP, or Azure, including a strong grasp of cloud-based engineering best practices.
- Experience with maintaining real-time and near real-time data pipelines or streaming services at high scale
- Proven ability to collaborate effectively with cross-functional teams, including data scientists, machine learning engineers, product managers, and other stakeholders. You can translate requirements into actionable technical tasks, communicate progress clearly, and adapt to feedback.
- Excellent problem-solving skills and the ability to work independently in a fast-paced environment. You can break down complex challenges into manageable steps and iterate on solutions, balancing immediate needs with long-term scalability.
- Familiarity with machine learning workflows and requirements to support MLE teams effectively. This includes feature development and serving at 50K+ QPS, offline/online equivalency, 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.
- #LI-PP1
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