Senior Machine Learning Engineer – AdTech Roku, Inc.

Coding Black Females Ltd; Cambridge; Remote; GBP 90,000 - 140,000; Full time

Job summary

Roku in the UK is seeking a senior, hands-on ML engineer to design, train, and ship ML models for ad relevance and bid/yield optimization, and to build production ML infrastructure for real-time, low-latency serving.

You'll work across Spark, Python, and Java, taking models from research through deployment, collaborating with Praveen Krishnaiah’s Native Ads Modelling team and the wider Ad Performance ML group. Based in the UK (Manchester-anchored).

Qualifications

• Senior-level ML engineering experience, ideally with exposure to ad tech, recommendation, or auction systems.
• Strong grounding in ML fundamentals, feature engineering, and model evaluation.
• Experience with Spark, Python, and Java in production ML settings.
• Comfort with production ML infrastructure and real-time, low-latency serving requirements.

Responsibilities

• Design, train, and ship ML models for ad relevance and bid/yield optimization.
• Build and maintain production ML infrastructure for real-time, low-latency serving.
• Work across Spark, Python, and Java to move models from research to production.
• Apply sound feature engineering, validation, and evaluation practices throughout the modelling lifecycle.
• Diagnose production ML issues such as model drift or long-tail classification problems.
• Communicate technical trade-offs clearly to engineering and cross-functional partners.

Skills

Senior ML engineering

Ad tech experience

Model deployment

Real-time systems

Education

Bachelor's degree in CS/ML/related field

Tools

Spark

Python

Java

Job description

What does the team work on?

Roku is the No. 1 TV streaming platform in the U.S., Canada, and Mexico with 70+ million active accounts. Roku pioneered streaming to the TV and continues to innovate and lead the industry. Roku enables our users to access millions of pieces of content — movies, episodes, news, sports, music, and channels — from all around the world, and our advertising systems connect advertisers to that audience at scale.

What is the role?

You’ll join Praveen Krishnaiah’s Native Ads Modelling team building the ML systems behind ad relevance, bid and yield optimization, and real-time, low-latency ad serving. This is a senior, hands-on ML engineering role spanning modelling, large-scale training, and production infrastructure — you’ll take models from idea through to serving at scale, working in Spark, Python, and Java. You’ll work closely with Praveen and the wider Ad Performance ML team. Based in the UK (Manchester-anchored).

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 you have built fluency across the agentic engineering toolchain — coding harnesses like Claude Code or Cursor, MCP servers, custom skills, or agent frameworks — and 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.

What are the responsibilities of the role?

• Design, train, and ship ML models for ad relevance and bid/yield optimization
• Build and maintain production ML infrastructure for real-time, low-latency serving
• Work across Spark, Python, and Java to move models from research to production
• Apply sound feature engineering, validation, and evaluation practices throughout the modelling lifecycle
• Diagnose production ML issues such as model drift or long-tail classification problems
• Communicate technical trade-offs clearly to engineering and cross-functional partners

What experience would help someone be successful in this role at Roku?

• Senior-level ML engineering experience, ideally with exposure to ad tech, recommendation, or auction systems
• Strong grounding in ML fundamentals, feature engineering, and model evaluation
• Experience with Spark, Python, and Java in production ML settings
• Comfort with production ML infrastructure and real-time, low-latency serving requirementsDemonstrated AI/agentic-tooling fluency per the section above
• Clear communicator who can explain reasoning and trade-offs, not just conclusions