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 developmentfrom data engineering and model development to scalable deploymentworking 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

36 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.

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