Machine Learning Engineer

We are recruiting a Senior Machine Learning Engineer to build and enhance intelligent systems that help the marketing team harness the power of data., * Data Analysis / Feature Engineering: Apply your expertise to identify and calculate features that can be leveraged by multiple use cases as well as models.

• Train Machine Learning Models: Use machine learning and statistical modelling techniques such as recommendations, reinforcement learning, decision trees, Bayesian analysis, neural networks and transformers to develop and evaluate algorithms to address business use cases and/or to improve product/system performance, quality and accuracy.
• Near Real-Time and Batch Inferencing: Use infrastructure like Spark and Ray to stand up inferencing services that integrate with operational/analytics workloads.
• ML Infrastructure: Help build a first-class machine learning platform from the ground up which helps manage the entire model lifecycle: feature engineering, model training/evaluation, versioning, deployment/online serving and monitoring prediction quality.
• Low-Level Systems Debugging, Performance Measurement & Optimisation: Performance measurement and optimisation on large production clusters., Roku fosters an inclusive and collaborative environment where teams generally work in the office Monday through Thursday. Fridays are generally flexible for remote work, except for employees whose specific roles or assigned office location require five days’ a week attendance.

The MarTech team at Roku is looking for a seasoned Senior Machine Learning Engineer with a strong background in machine learning and production systems. This role sits in a team working on high-impact problems across marketing and advertising, where machine learning is being applied to improve customer decisioning, creative and campaign performance, and the systems that support experimentation and optimisation.

Examples of such problems include creative personalisation for customers, improving ad relevance and targeting, inferring demographics, yield optimisation, and many more. Employees in this role are expected to apply knowledge of experimental methodologies, statistics, recommendations, reinforcement learning, optimisation, probability theory, and machine learning, using code for statistical analysis and tool building, using both general-purpose software and statistical languages.

The ideal candidate will have endless curiosity and can pair a global mindset with locally relevant execution. You should be a gritty problem solver and self-starter who can work effectively with engineering, product, data science, and commercial stakeholders across Roku. The successful candidate will display a balance of hard and soft skills, including the ability to respond quickly to changing business needs., * First-hand experience in applied machine learning on real recommendations use cases (brownie points for productionised sequential learning use cases!).

• Experience with ML/distributed ML frameworks like Ray, Spark-MLlib, TensorFlow etc.
• Experience with real-time scoring/evaluation of models with low latency constraints.
• Great coding skills and strong software development experience (we use Spark, Python and Java a lot).
• Ability to work with large-scale computing frameworks, data analysis systems and modelling environments. Examples include technologies like Spark, Hive, NoSQL stores etc.
• Bachelor’s, Master’s or PhD in Computer Science, Statistics or a related field.
• Ad-tech/Mar-tech background is a plus.

Roku pioneered TV streaming and continues to innovate and lead the industry. The Roku Channel has us well-positioned to help shape the future of streaming. Continued success relies on building customer relationships with Roku that delight and engage them.

Within Advertising Engineering, the MarTech team builds the products, services, and machine learning systems that help Roku deliver the right communication, creative, and marketing experience to the right customer at the right time on the right marketing channel. The team is focused on turning data, experimentation, and intelligent decisioning into production systems that improve marketing effectiveness at scale.

Our mission is to build cutting-edge advertising technology and marketing products to support and grow a sustainable advertising business. The team owns server technologies, data platforms, and cloud services that power advertising and marketing use cases., How will I use AI at Roku?

At Roku, we don’t just use AI, we work with it. AI agents and smart tools help power drafts, analysis, and repetitive workflows, while our people bring direction, judgment, and accountability. We’re looking for curious, adaptable builders who can show how they’ve used AI or automation to move faster, raise the bar, and scale their impact.

We value your AI skills if have built fluency across the agentic engineering toolchain - coding harnesses like Claude Code or Cursor, MCP servers, custom skills, or agent frameworks. And you can describe projects where you shipped real work with these tools. You know how to drive an agent, verify its output, and ramp on an unfamiliar codebase with an agent helping you., Roku is a great place for people who want to work in a fast-paced environment where everyone is focused on the company’s success rather than their own. We try to surround ourselves with people who are great at their jobs, who are easy to work with, and who keep their egos in check. We appreciate a sense of humor. We believe a fewer number of very talented folks can do more for less cost than a larger number of less talented teams. We’re independent thinkers with big ideas who act boldly, move fast and accomplish extraordinary things through collaboration and trust. In short, at Roku you’ll be part of a company that’s changing how the world watches TV.

We have a unique culture that we are proud of. We think of ourselves primarily as problem-solvers, which itself is a two-part idea. We come up with the solution, but the solution isn’t real until it is built and delivered to the customer. That penchant for action gives us a pragmatic approach to innovation, one that has served us well since 2002.