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Media.net
Software Development Engineer II (Machine Learning) | R3037
Media.net
Bengaluru•On-site•3-6 Yrs•Jun 18
Job description
MLData ProcessingAd Tech
Overview
As an SDE II – Machine Learning, you will build ML-driven systems.
Responsibilities
- 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.
- 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.
- 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.
- Languages: Python, Java, Node.js
- ML/Big Data: Apache Spark, Hadoop, TensorFlow/PyTorch, Kafka
- Databases: SQL, MongoDB, Redis, Elasticsearch
- Cloud: GCP or similar
Qualifications
- About Media.net
- Media.net is a leading global ad tech company, building innovative products and solutions for advertisers and publishers. Media.net creates the most transparent and efficient path for ad budgets to become publisher revenue, while delivering meaningful value to advertisers, publishers, app developers, and end users across the open web.
- We are one of the world’s largest independent contextual advertising businesses, with one of the industry’s most comprehensive advertising technology portfolios. Media.net powers major publishers and ad-tech platforms across formats, including display, video, mobile, native, search, and local. Our platform manages high-quality ad supply across 500,000+ websites and is licensed by the biggest publishers, ad networks, and technology partners worldwide.
- Beyond our core advertising business, Media.net also operates at scale in performance-driven consumer acquisition, app development and distribution, and strategic technology investments. These efforts are supported by one of the world’s largest independent software development engines, enabling us to innovate rapidly and build products used across global markets.
- Media.net is home to 1,300+ employees, with key operations across North America, Europe, and Asia. Our US headquarters is in New York, and our global headquarters is in Dubai. Across 50+ demand partners, 21K+ publishers, and a growing portfolio of products and services, we take great pride in driving trusted, transparent, and scalable results.
- 3–6 years of hands-on experience in software development and ML engineering.
- Strong programming and debugging skills, preferably in Python and Java.
- 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.
- Exposure to auction theory or game-theoretic modeling.
- Work on high-impact problems that power ad delivery at scale.
- Flexible work hours, modern office spaces, and supportive teams.
- Full-stack ownership and end-to-end involvement in projects.
- Culture that values innovation, autonomy, and deep tech.
Nice to have
- Prior experience with ad tech, recommender systems, or real-time bidding.
- Publications or contributions to ML research or open-source projects.