Senior Machine Learning Engineer; AdTech

Requirements

• We're looking for seasoned engineers with a background in machine learning to aid in this mission
• Bachelors, Masters, or PhD in Computer Science, Statistics, or a related field
• Demonstrated depth in applied machine learning on production systems — typically 6+ years in industry, although we value PhD experience as meaningful acceleration
• Great coding skills and strong software development experience (we use Spark, Python, Java)
• Familiarity with real-time evaluation of models with low latency constraints
• Familiarity with distributed ML frameworks such as Spark-MLlib, Tensor Flow, etc
• Ability to work with large scale computing frameworks, data analysis systems, and modelling environments. Examples include Spark, Hive, No
  
  SQL stores such as Aerospike and ScyllaDB
• Ad tech background is a plus

• We’re on a mission to build cutting-edge advertising technology that empowers businesses to run sustainable and highly-profitable campaigns
• The Ad Performance team owns server technologies, data, and cloud services aimed at improving the ad experience
• Examples of problems include improving ad relevance, inferring demographics, yield optimisation, and many more
• Employees in this role are expected to apply knowledge of experimental methodologies, statistics, optimisation, probability theory, and machine learning using both general purpose software and statistical languages
• 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
• Data analysis and feature engineering:
  Apply your expertise to identify and generate features that can be leveraged by multiple use cases and models
• 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
• Production operations:
  Low‑level systems debugging, performance measurement, and optimisation on large production clusters
• Collaboration with cross‑functional teams:
  Partner with product managers, data scientists, and other engineers to deliver impactful solutions
• Staying ahead of the curve:
  Continuously learn and adapt to emerging technologies and industry trends