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Applications open Full-Time Job

Software Engineer I - Backend

Abnormal AI

  • Bengaluru, India
  • Competitive Package
  • 1-3 Years
  • Posted Yesterday
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About the role

About The Role

This position exists to build and improve the configuration platform owned by the Config Platform team, within the Multi-Product Platform (MPP) division. The role plays a critical part in helping the team deliver on its mission: making configuration easier to access, reuse, and operate across Abnormal, so product teams can spend more time on customer-facing capabilities and less on infrastructure plumbing. You will contribute directly to the backend systems that control how Abnormal products are configured.

What you will do

Abnormal AI is looking for a driven and growth-oriented Backend Software Engineer to join the Config Platform team. The team builds and maintains the shared systems behind product settings, feature toggles, and tenant configuration. As a member of the team, you will build and scale backend services, help make platform capabilities more reliable, scalable, and observable, and work with engineers across product teams to turn their configuration needs into maintainable software.

You’ll work across APIs, backend services, and data workflows, and take part in code reviews, testing, releases, and operational learning as you grow toward owning well-scoped components end to end. We’re looking for candidates who are eager to learn, excited to work collaboratively, and motivated by building foundations that many engineering teams rely on.

Must Haves

  • 1+ years of experience as a software engineer with a strong foundation in coding and problem-solving.
  • Hands-on experience in backend development using Python or Golang, with exposure to cloud-native environments.
  • Solid understanding of databases, REST APIs, and AWS or any other cloud service provider.
  • Familiarity with LLMs and AI development tools such as Cursor, GitHub Copilot, or Claude.
  • Experience owning a feature or well-defined problem end to end.
  • Experience working with distributed teams, proficient in asynchronous and written communication.
  • Experience building features and components and operating them, balancing feature development with operational tasks.

Nice to Haves

  • Familiarity with AWS services and deployment practices.
  • Exposure to configuration management, feature flags, or multi-tenant systems.

🚀 Ready to be part of AI transformation at Abnormal AI? Apply Now!

Once you apply, you’ll be invited to our AI-powered Development Challenge, where you’ll gain hands-on experience with AI-powered tools like Cursor and Copilot to build real-world application features. This challenge is a take-home assignment, requiring 2-4 hours of work to be completed within one week - apply when you’re ready!

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.

Eligibility & process

Who can apply

Employment type: Full-time

Skills & stack

Python Go REST APIs AWS Git Generative AI / LLMs Data Structures & Algorithms Python/Java

Selection process

Details are shared by the recruiter after shortlisting.

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Python, Go, REST APIs, AWS, Git, Generative AI / LLMs, Data Structures & Algorithms, Python/Java) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Abnormal AI Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Abnormal AI as a Software Engineer I - Backend?
Highlight Abnormal AI's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Abnormal AI Competitive Package
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