Senior Software Engineer - Business Applications and Machine Learning

Roku•Boston, MA

4d•Hybrid

About The Position

Drive the AI-native transformation of Roku's advertising business operations, identifying high-leverage opportunities and influencing roadmap, architecture, and system design across multiple teams. Work closely with advertising business stakeholders to identify needs, surface high-impact opportunities, and translate operational pain points into ML and agentic AI solutions. Design and ship production ML and agentic systems end-to-end, including recommendation, personalization, ranking, forecasting, anomaly detection, and multi-agent or agent-to-agent workflows, embedded directly within product and operational systems where they drive decisions. Apply GenAI and LLMs to deliver measurable value by building and scaling Retrieval Augmented Generation (RAG) pipelines, prompt orchestration, evaluation frameworks, and guardrails, with a focus on reliability and outcomes over hype. Translate ambiguous business problems into production solutions, partnering with engineers, data scientists, product managers, and business teams to ground ML and AI work in real operational impact. Build and operationalize evaluation loops covering precision and recall, calibration, drift detection, and human-in-the-loop systems, and define the dashboards and Service Level Objectives (SLO) that tie model performance to business outcomes. Start from the needs of internal engineers and business operators, bringing a creative and strategic approach to simplifying systems and introducing practical, high-impact solutions. Provide technical leadership across the broader ML and AI surface area, raising the bar for engineering rigor, mentoring engineers, and shaping how the organization thinks about AI-native systems.

Requirements

Master's or PhD in Computer Science, Mathematics, Statistics, or a related technical field, or equivalent practical experience.
8+ years of hands-on engineering experience, with a track record of technically leading and delivering multiple complex ML and AI initiatives.
Strong foundation in machine learning and statistical modeling, including clustering, classification, regression, decision trees, neural networks, SVMs, and anomaly detection, with deep understanding of supervised and unsupervised learning, feature engineering, model evaluation, bias-variance tradeoffs, and offline vs. online metrics.
Proven experience building and scaling production ML and AI systems, including LLMs, RAG architectures, embeddings, retrieval-based systems, and multi-agent or agent-based system design.
Hands-on experience designing, training, tuning, and deploying models for ranking, prediction, recommendation, forecasting, classification, or NLP use cases.
Demonstrated technical leadership, including setting direction, influencing across teams, and elevating the work of other engineers.

Nice To Haves

Experience in advertising, marketplaces, e-commerce, travel, or similar data-rich, decision-driven platforms is a strong plus.

Responsibilities

Drive the AI-native transformation of Roku's advertising business operations, identifying high-leverage opportunities and influencing roadmap, architecture, and system design across multiple teams.
Work closely with advertising business stakeholders to identify needs, surface high-impact opportunities, and translate operational pain points into ML and agentic AI solutions.
Design and ship production ML and agentic systems end-to-end, including recommendation, personalization, ranking, forecasting, anomaly detection, and multi-agent or agent-to-agent workflows, embedded directly within product and operational systems where they drive decisions.
Apply GenAI and LLMs to create measurable value, building and scaling Retrieval Augmented Generation (RAG) pipelines, prompt orchestration, evaluation frameworks, and guardrails, with a focus on reliability and outcomes over hype.
Translate ambiguous business problems into production solutions, partnering with engineers, data scientists, product managers, and business teams to ground ML and AI work in real operational impact.
Build and operationalize evaluation loops covering precision and recall, calibration, drift detection, and human-in-the-loop systems, and define the dashboards and Service Level Objectives (SLO) that tie model performance to business outcomes.
Start from the needs of internal engineers and business operators, bringing a creative and strategic approach to simplifying systems and introducing practical, high-impact solutions.
Provide technical leadership across the broader ML and AI surface area, raising the bar for engineering rigor, mentoring engineers, and shaping how the organization thinks about AI-native systems.