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# Senior Machine Learning Engineer - AdTech

- **Company:** Roku, Inc.
- **Location:** Cambridge, MA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Data Analysis, Artificial Neural Networks, Software Debugging, Apache Hive, Python (Programming Language), Logistic Regression, Machine Learning, NoSQL, Cloud Services, Tensorflow, Software Engineering, Management of Software Versions, Aerospike, Feature Engineering, Apache Spark, Yield Optimization, Information Technology, Low Latency, Machine Learning Operations, Software Coding
- **Published:** June 6, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=b45844ce996d8382

## About the Role

Do you have a Master's degree?, * 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, TensorFlow, etc.
 * 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.
 * Ad tech background is a plus.

## Description

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. We're looking for seasoned engineers with a background in machine learning to aid in this mission. 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.


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