InMobi

Job Summary

We're looking for a Senior Backend Software Development Engineer (SDE 2 and SDE 3) with deep technical expertise in building and operating production systems at scale. You'll own entire components/services within microservices and data pipelines that handle ~200K queries per second and process petabytes of data daily. This role demands hands-on coding excellence, strong Low-Level Design (LLD) skills, and complete ownership—from component architecture through infrastructure management to cost optimization. You'll architect solutions, lead technical decisions, and drive operational excellence while shipping code daily.

Key Responsibilities:

• Component Ownership: Own one or more critical components/services end-to-end—responsible for architecture, development, deployment, operations, and evolution
• Technical Ownership: Own the entire lifecycle of your components—design, implementation, testing, deployment, monitoring, incident response, and continuous improvement
• Low-Level Design: Create detailed technical designs (LLD) for complex systems—defining data models, APIs, concurrency patterns, and failure modes
• Hands-on Development: Write production-grade code daily—this is not a purely architectural role; you'll be deep in the codebase
• Infrastructure Ownership: Own and operate the infrastructure your components run on—capacity planning, scaling, reliability improvements
• Cost Management: Drive cost optimization for owned components—analyze spending, identify waste, implement efficient architectures
• Scale & Performance: Build and optimize systems handling 200K+ QPS and petabyte-scale data processing
• Observability: Design and implement comprehensive monitoring, alerting, and debugging capabilities for owned components
• Incident Leadership: Lead incident response for your components and related services, conduct post-mortems, drive systemic improvements
• On-Call Excellence: Participate in on-call rotations and ensure your components are operationally sound (runbooks, alerts, dashboards)
• Technical Roadmap: Define and drive the technical roadmap for your owned components—balancing feature development, tech debt, and operational improvements
• Technical Mentorship: Guide junior and mid-level engineers on system design, code quality, and production best practices
• Cross-functional Collaboration: Work with product, infra, and other eng teams to define requirements and deliver solutions
• Agile Execution: Break down complex projects, deliver incrementally in daily cadence, iterate based on feedback

Required Qualifications:

• Experience: 2+ years building and operating backend systems in production environments at scale
• Education: B.E./B.Tech in Computer Science or equivalent practical experience
• Component Ownership: Proven track record of owning significant components or services from inception to maturity—demonstrable end-to-end ownership
• Low-Level Design (LLD): Proven ability to create detailed technical designs—data structures, algorithms, API contracts, concurrency models, failure handling
• Programming Mastery: Expert-level proficiency in at least one modern language (Go, Python, Java, NodeJS etc.) with track record of writing maintainable, performant production code
• Databases: Deep hands-on experience with SQL and NoSQL databases—schema design, query optimization, indexing strategies, operational troubleshooting
• Microservices at Scale: Extensive experience building, deploying, and operating microservices handling high throughput and large data volumes
• Data Pipelines: Strong background designing and running data processing pipelines at scale (batch and/or streaming)
• Observability: Expert understanding of metrics, logging, tracing, and alerting—you know how to make systems debuggable
• Production Operations: Significant experience with incident response, on-call rotations, debugging live issues under pressure
• Infrastructure Knowledge: Hands-on experience managing infrastructure, understanding resource utilization, capacity planning
• Cost Consciousness: Experience analyzing and optimizing infrastructure costs at scale
• Distributed Systems: Strong fundamentals in distributed systems, concurrency, consistency models, and failure scenarios
• Accountability: Track record of taking full ownership—from design through deployment to ongoing operations and improvements

Preferred Qualifications:

• Experience with cloud platforms (GCP, AWS, or Azure) including cost management tools
• Kubernetes and container orchestration at scale
• Infrastructure as Code (Terraform, Pulumi, etc.)
• Streaming data systems (Kafka, Pub/Sub, Kinesis, Flink, etc.)
• SRE principles and reliability engineering practices
• Experience with FinOps or infrastructure cost optimization
• Performance profiling and optimization (CPU, memory, I/O)
• Technical leadership experience including mentorship of teams and driving multi-component initiatives
• Open source contributions or recognized technical writing

This role is at Glance

What you will be doing

We are looking for a Data Scientist who can operate at the intersection of classical machine learning, large-scale recommendation systems, and modern agentic AI systems.

You will design, build, and deploy intelligent systems that power Glance’s personalized lock screen and live entertainment experiences. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.

Your responsibilities will include:

Classical ML & Recommendation Systems

• Design and develop large-scale recommendation systems using advanced ML, statistical modeling, ranking algorithms, and deep learning.
• Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
• Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts.
• Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities.

Agentic Systems & Next-Gen AI

• Build and experiment with agentic AI systems that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
• Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding, content classification, and user preference modeling.
• Design intelligent agents that can automate repetitive decision-making tasks—e.g., candidate generation tuning, feature selection, or context-aware content curation.
• Explore reinforcement learning, contextual bandits, and self-improving systems to power next-generation personalization.

Cross-functional impact

• Collaborate with Designers, UX Researchers, Product Managers, and Software Engineers to integrate ML and GenAI-driven features into Glance’s consumer experiences.
• Contribute to Glance’s ML/AI thought leadership—blogs, case studies, internal tech talks, and industry conferences.
• Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
• Plus: Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.

The experience we need

We are seeking candidates with deep expertise in ML, recommendation systems, and a strong appetite for building agentic AI systems.

You should have experience with:

• Large-scale ML and recommendation systems (collaborative filtering, ranking models, content-based approaches, embeddings).
• Classical ML and deep learning techniques across NLP, sequence modeling, RL, clustering, and time series.
• Experience in deploying ML workflows/models in production system
• Big data processing (Spark, distributed data systems) and cloud computing.
• Designing end-to-end ML solutions—from prototype to production.
• Plus: Building or experimenting with LLMs, generative models, and agentic AI workflows (e.g., autonomous evaluators, self-improving pipelines, automated experiment agents).

We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact.

Qualifications

• Bachelor’s/Master’s in Computer Science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
• 4+ years of industry experience in ML/Data Science, ideally in large-scale recommendation systems or personalization.
• Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
• Expertise with algorithms in NLP, Reinforcement Learning, Time Series, and Deep Learning, applied on real-world datasets.
• Proficient in Python and comfortable with statistical tools (R, NumPy, SciPy, PyTorch/TensorFlow, etc.).
• Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
• Comfortable working in cross-functional teams.
• Familiarity with privacy-preserving ML and identity-less ecosystems (especially on iOS and Android).
• Excellent communication skills with the ability to simplify complex technical concepts.