Machine Learning Engineer

Role Overview

As an SDE II – Machine Learning, you will be a core contributor in designing, building, and scaling ML-driven systems that power our real-time ad platforms. You'll be responsible for full-stack ML development—from data engineering and model development to scalable deployment—working closely with product, data science, and engineering teams.

What You'll Do

· Build and deploy machine learning models for ranking, bid optimization, and click-through rate prediction.

· Design scalable and fault-tolerant data pipelines and services that serve real-time and batch ML workloads.

· Work with large volumes of structured and unstructured data to extract meaningful patterns. · Collaborate with data scientists to convert prototypes into production-ready systems.

· Build systems to intelligently target ads and content by combining contextual and behavioral signals.

· Use LLM learning to improve ad relevance, page understanding, and user targeting.

· Continuously experiment and optimize models based on user feedback and system performance.

Some Interesting Challenges You'll Solve

· Predicting CTRs and revenue across millions of unique URLs and topics in real-time.

· Solving cold-start problems with sparse data using explore-exploit frameworks.

· Matching contextual and behavioral data for enhanced user targeting.

· Designing real-time bidding systems that optimize for revenue and win rate.

· Leveraging LLMs/NLP to extract intent and context from web content.

Tech Stack You'll Work With

· Languages: Python, Java, Node.js

· ML/Big Data: Apache Spark, Hadoop, TensorFlow/PyTorch, Kafka

· Databases: SQL, MongoDB, Redis, Elasticsearch

· Cloud: GCP or similar

What We're Looking For

· 3–6 years of hands-on experience in software development and ML engineering.

· Strong programming and debugging skills, preferably in Python and Java.

· Experience building and deploying ML models in production environments.

· Solid understanding of ML algorithms (e.g., decision trees, gradient boosting, deep learning).

· Hands-on experience with large-scale data processing tools (e.g., Spark, Hadoop).

· Ability to design low-latency, high-throughput systems.

· Strong problem-solving and analytical skills.

Bonus Points

· Prior experience with ad tech, recommender systems, or real-time bidding.

· Publications or contributions to ML research or open-source projects.

· Experience with NLP, LLMs, or Information Retrieval.

· Exposure to auction theory or game-theoretic modeling.

#IIT#NIT#IIIT#IISc#Jadavpur university#VIT#BITS Pilani