About
Aarki is an AI-driven company specializing in mobile advertising solutions designed to fuel revenue growth. We leverage AI to discover audiences in a privacy-first environment through trillions of contextual bidding signals and proprietary behavioral models. Our audience engagement platform includes creative strategy and execution. We handle 5 million mobile ad requests per second from over 10 billion devices, driving performance for both publishers and brands. We are headquartered in San Francisco, CA, with a global presence across the United States, EMEA, and APAC. Role Overview We are seeking a Staff Machine Learning Engineer to build, deploy, and test machine learning models that power real-time advertising use cases including bidding, ranking, pacing, and fraud. This role focuses on delivering low-latency, high-throughput models into production with a focus on accuracy, stability, and explainability. You will work at the intersection of machine learning and real-time bidding, partnering with our engineering team to serve millions of predictions per second in latency-sensitive environments. The ideal candidate brings strong systems programming experience and a production-first mindset. What will you do? Own the design, development, and operation of end-to-end machine learning models, including model training, evaluation, and deployment. Partner with engineering to productize models and ensure reliable deployments. Optimize inference performance across the stack, including latency and calibration issues. Design, execute, and analyze A/B tests and clearly communicate results. Perform root-cause analyses on model behavior, identify causes of prediction changes, and implement preventative measures. Design and implement new ML features across our model training and inference pipelines. Optimize our model architectures for a combination of inference speed and accuracy. What are we looking for? 8+ years of hands-on experience building and operating production machine learning systems Strong Python and Spark for large-scale processing (on-prem/YARN environments preferred) Proficiency in at least one systems language (e.g., C++, Java, Rust, Go) and strong experience with Python for ML development. Experience with data preparation and model training on large-scale data sets. Professional experience with deep learning frameworks. Familiarity with model serialization for serving, e.g., ONNX. Experience working with on-prem deployments of open source tools including Spark, ClickHouse, and Redash is a plus. Nice-to-Have Exposure to online inference systems, gRPC/REST model endpoints, or streaming features (Kafka/Flink). Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization.
Languages
- English
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