Senior Software Engineer, Machine Learning

About this role

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Skills you'll use

What you'll do

• p]:inline" data-streamdown="list-item"> ML infrastructure: Help build a first-class machine learning platform from the ground up which manages the entire model lifecycle - feature engineering, model training, versioning, deployment, online serving/evaluation, and monitoring prediction quality
• p]:inline" data-streamdown="list-item"> Data analysis and feature engineering: Apply your expertise to identify and generate features that can be leveraged by multiple use cases and models
• p]:inline" data-streamdown="list-item"> Model training with batch and real-time prediction scenarios: Use machine learning and statistical modelling techniques such as Decision Trees, Logistic Regression, Neural Networks, Bayesian Analysis and others to develop and evaluate algorithms for improving product/system performance, quality, and accuracy
• p]:inline" data-streamdown="list-item"> Production operations: Low-level systems debugging, performance measurement, and optimisation on large production clusters
• p]:inline" data-streamdown="list-item"> Collaboration with cross-functional teams: Partner with product managers, data scientists, and other engineers to deliver impactful solutions
• p]:inline" data-streamdown="list-item"> Staying ahead of the curve: Continuously learn and adapt to emerging technologies and industry trends
• p]:inline" data-streamdown="list-item">Bachelors, Masters, or PhD in Computer Science, Statistics, or a related field
• p]:inline" data-streamdown="list-item">Experience in applied machine learning on real use cases (bonus points for ad tech-related use cases). Minimum 10 years of total experience

Who can apply

• p]:inline" data-streamdown="list-item">Great coding skills and strong software development experience (we use Spark, Python, Java)
• p]:inline" data-streamdown="list-item">Familiarity with real-time evaluation of models with low latency constraints
• p]:inline" data-streamdown="list-item">Familiarity with distributed ML frameworks such as Spark-MLlib, TensorFlow, etc
• p]:inline" data-streamdown="list-item">Ability to work with large scale computing frameworks, data analysis systems, and modelling environments. Examples include Spark, Hive, NoSQL stores such as Aerospike and ScyllaDB
• p]:inline" data-streamdown="list-item">Ad tech background is a plus
• p]:inline" data-streamdown="list-item">AI growth mindset / AI Literacy

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