Senior Machine Learning Engineer

As a Machine Learning Engineer at Appnext, your primary responsibility will be to build end-to-end machine learning pipelines that operate at a massive scale. This includes tasks such as data investigation, ingestions, model training, deployment, monitoring, and continuous optimization. You will play a crucial role in ensuring that each pipeline delivers measurable impact through experimentation, high-throughput inference, and seamless integration with business-critical systems. This position will involve a combination of 70% machine learning engineering and 30% algorithm engineering and data science. Responsibilities: - Build ML pipelines that train on real big data and perform on a massive scale. - Handle a massive responsibility, Advertise on lucrative placement (Samsung appstore, Xiaomi phones, TrueCaller). Train models that will make billions of daily predictions and affect hundreds of millions users. - Optimize and discover the best solution algorithm to data problems, from implementing exotic losses to efficient grid search. - Validate and test everything. Every step should be measured and chosen via AB testing. - Use observability tools to own your experiments and pipelines. - Be Frugal and optimize the business solution at minimal cost. - Advocate for AI and be the voice of data science and machine learning, answering business needs. - Build future products involving agentic AI and data science. - Affect millions of users every instant and handle massive scale. Requirements: - MSc in CS/EE/STEM with at least 5 years of proven experience (or BSc with equivalent experience) as a Machine Learning Engineer: strong focus on MLOps, data analytics, software engineering, and applied data science. - Hyper communicator with the ability to work with minimal supervision and maximal transparency. Must understand requirements rigorously, while frequently giving an efficient honest picture of his/hers work progress and results. Flawless verbal English. - Strong problem-solving skills, drive projects from concept to production, working incrementally and smart. Ability to own features end-to-end, theory, implementation, and measurement. Articulate data-driven communication is also a must. - Deep understanding of machine learning, including the internals of all important ML models and ML methodologies. - Strong real experience in Python, and at least one other programming language (C#, C++, Java, Go). Ability to write efficient, clear, and resilient production-grade code. Flawless in SQL. - Strong background in probability and statistics. Experience with tools and ML models. Experience with conducting A/B tests. - Experience with using cloud providers and services (AWS) and python frameworks: TensorFlow/PyTorch, Numpy, Pandas, SKLearn (Airflow, MLflow, Transformers, ONNX, Kafka are a plus). - AI/LLMs assistance: Candidates have to hold all skills independently without using AI assist. With that candidates are expected to use AI effectively, safely and transparently. Preferred: - Deep Knowledge in ML aspects including ML Theory, Optimization, Deep learning tinkering, RL, Uncertainty quantification, NLP, classical machine learning, performance measurement. - Prompt engineering and Agentic workflows experience. - Web development skills. - Publication in leading machine learning conferences and/or medium blogs.,