Currently pursuing a PhD in Computer Science, Software Engineering, Machine Learning, Data Science, or a related technical field. Strong foundational knowledge in data structures, algorithms, and object-oriented programming. Proficiency in at least one programming language commonly used in machine learning or backend development (e.g., Python, Java, Scala, C++). Familiarity with version control systems (e.g., Git). Knowledge of database systems (SQL/NoSQL) and data warehousing concepts.
Excellent problem-solving and analytical skills, with an ability to analyze complex data. Understanding of fundamental machine learning concepts and statistical methods, with experience using machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn). Exposure to model optimization techniques such as quantization, tensor parallelism, and inference optimization (e.g., ONNX Runtime, TensorRT, vLLM). Familiarity with modern neural network architectures (e.g., Transformers, DNNs, RNNs) and training pipelines.
Awareness of reinforcement learning, explore/exploit strategies, and bandit-based optimization concepts. Exposure to GenAI and agentic system concepts and their application to machine learning workflows. Familiarity with large-scale data processing technologies (e.g., Spark, Hadoop, Flink) and distributed systems (e.g., Ray, high-throughput RPC systems) to support scalable inference and data workloads. Experience with or exposure to A/B testing infrastructure and performance measurement at scale.
Prior coursework or projects related to advertising technology, recommender systems, natural language processing, computer vision, or privacy-preserving ML techniques (e.g., federated learning). Ability to communicate effectively, both written and verbal, with technical and non-technical multi-functional teams.