Tubi

Full-stack Engineer

• Define and drive the technical architecture of the AI platform
• Own the platform's most complex subsystems
• Establish clear system boundaries and integration contracts
• Ship consistently and turn ambiguous requirements into working software
• Mature the platform's AI systems from early-stage to production-grade
• Raise the engineering bar through code review and design discussions

Staff Software Engineer – Internal Tools

• Own the technical roadmap and architecture for a Growth area with large amounts of ambiguity: driving direction across web, mobile web, and smart TV platforms, and influencing the team to invest in new projects.
• Solve challenging, ambiguous problems with a focus on scalability and performance; proactively identify systemic issues and propose innovative solutions.
• Lead and drive innovation in building experiment-driven features; independently design, implement, and interpret a series of A/B experiments that move growth metrics.
• Lead and coordinate major rollouts and releases: including cross-team coordination, migrations, and phased releases of major initiatives across Web and OTT apps.
• Establish team-wide quality and engineering standards; set the bar for code quality and front-end best practices.
• Identify and implement improvements in shared UI components, platform-specific optimizations, and overall front-end infrastructure.
• Influence and align cross-functional teams across multiple pods/domains (Backend, Product, Data Science, Design, UXR) toward a common objective.
• Mentor multiple engineers, providing structured guidance to foster both technical and professional growth.
• Stay up-to-date with the latest web development trends and technologies, lead their adoption on the team, and evaluate their potential impact on the product.

Staff Software Engineer – Growth

Account Manager

• Source, pitch, and secure partnerships with Canadian creators
• Support broader strategic content partnerships with streaming platforms
• Play a key role in shaping the Tubi Canada content strategy
• Manage ongoing relationships with creators and partners
• Track and analyze partner performance

Senior Manager, Creator & Content Partnerships

Blockchain Engineer

• Design and build scalable, high throughput, and low latency distributed systems using Scala
• Build reusable components and services that serve various ML applications like Personalization, Search, Ads and Exploration
• Partner closely with ML engineers to understand their challenges and limitations and develop scalable solutions to address them. Proactively recommend solutions to keep our ML Inference stack state of the art.
• Take a data driven approach to identifying & optimizing latency, cost, and efficiency of our infra. Lead large scale cross functional refactorings if necessary
• Mentor other engineers on the team on system design, effective incident management, interviewing, leveraging LLMs for work, etc.
• Collaborate with ML, Product, and cross functional engineering teams to define the long term vision and architecture for ML Infrastructure at Tubi.

Software Engineer, ML Infra, Distributed Systems – Staff, Principal

• Work with product management and other stakeholders (Backend, Product, and UI/UX) to iterate on new growth-related features
• Lead the technical architecture and implementation of scalable and resilient applications that run on multiple platforms
• Lead and drive innovation in building experiment-driven features that push the boundaries of user experience in streaming
• Consistently ship features and improvements across Web and OTT apps with minimal guidance
• Identify and implement improvements in shared UI components, platform-specific optimizations, and overall front-end infrastructure
• Take ownership of the codebase and proactively identify opportunities for refactoring and development process improvement
• Mentor and collaborate with fellow engineers, sharing technical expertise and contributing to a culture of continuous learning and improvement
• Stay up-to-date with the latest web development trends and technologies

Senior Software Engineer, Growth

Content Marketing Manager

• Work on systems that sit at the heart of Tubi's business
• Partner with the Video Team, Content Operations, Business Affairs, Ad Tech, and external creators/refiners
• Power seamless content ingestion
• Run one of streaming's largest video processing workloads
• Automate the content lifecycle end to end
• Build the content data model and high-throughput services
• Design pipelines and APIs for AI-native operations
• Drive down streaming costs through architectural decisions
• Craft production-grade frontends for creators, partners, and Content Ops

Senior Software Engineer – Content Platforms Engineering

SDET

• Design and lead test strategies for streaming workflows, playback systems, and AI-powered features.
• Test across platforms (web, mobile, and connected TV) to ensure functional parity and playback stability.
• Validate streaming performance—including ABR logic, encoding pipelines, and DRM integrations—under diverse real-world conditions.
• Debug with precision using tools like Charles Proxy, Chrome DevTools, ADB, and Xcode. Collaborate with data and ML teams to validate AI model updates, recommendations, and personalization accuracy.
• Leverage AI-assisted QA tools to enhance regression coverage, UI validation, and anomaly detection.
• Contribute to automation and CI/CD frameworks, driving faster, more reliable releases.
• Help drive a shift-left testing approach by engaging early in the software development lifecycle, partnering with product managers, engineers, and data scientists to identify quality risks, define test strategies, and ensure testability during requirements and design phases.
• Oversee QA deliverables for multiple concurrent releases and ensure seamless sign-off for production launches.
• Monitor live environments for playback or recommendation anomalies post-release and escalate issues promptly.
• Continuously improve QA processes, metrics, and reporting for streaming and AI validation.

Senior QA Automation Engineer

DevOps Engineer

• Lead, mentor, and grow a team of Site Reliability Engineers
• Define and drive the multi-year technical strategy and vision
• Own the end-to-end availability, performance, and efficiency of critical user-facing services
• Evolve incident response practices
• Own the SRE budget, tooling, and headcount
• Act as a key influencer and strategic partner to leaders in Software Engineering, Product Management, and Infra/Sec
• Developing and executing the strategy for integrating AIOps

Senior Manager, Site Reliability Engineering

Machine Learning Engineer

• Design, develop, and implement recommendation systems and algorithms for a global audience
• Conduct deep dives into algorithmic components and systems, ensuring that models are optimized for both performance and scalability across multiple regions and product areas
• Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment
• Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and provide high-quality user experiences
• Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement

Senior Machine Learning Engineer

Platform Engineer

• Spark-on-Kubernetes — EKS-based compute platform for Spark workloads: cluster configuration, Pod Identity IAM, job environment setup, Kustomize overlays, and shadow canary validation
• Event ingestion — Rust services and Flink jobs processing billions of events per day over Kinesis; throughput, reliability, on-call response, and AI-assisted operational tooling to reduce toil
• Platform infrastructure — Terraform modules for environment provisioning, cross-account AWS IAM, ARC runner infrastructure, and CI/CD for data platform changes
• Feature store and ML compute — Flink-based real-time feature pipelines feeding a large-scale MemoryDB cluster; GPU capacity governance and Databricks multi-environment operations for ML training workloads
• Workflow orchestration and CDC — Airflow-based DAG deployment, change data capture pipeline operations, and data quality monitoring

Senior Data Platform Engineer

QA Automation Engineer

• Design, implement, and maintain automated tests for mobile development (Android & iOS)
• Contribute to the development and optimization of cross-platform automation frameworks.
• Write and maintain test scripts in JavaScript/TypeScript, using frameworks such as Puppeteer, Appium, WebDriverIO, Selenium.
• Ensure test cases are integrated into CI/CD pipelines and provide reliable feedback on product quality.
• Help identify flaky tests, investigate root causes, and improve test stability.
• Collaborate with developers and QA engineers to clarify requirements and improve test strategies.
• Participate in code reviews and follow best practices for test automation.

Senior Automation Engineer

Android Engineer

• Build elegant, performant, and reliable Android experiences using Kotlin and modern Android development practices
• Lead the full lifecycle of mobile features, from technical design and implementation through testing, release, measurement, and iteration
• Collaborate closely with Product Managers, Designers, Backend Engineers, Data Scientists, QA, and other partners to deliver user-focused solutions
• Improve app performance, stability, observability, accessibility, and developer productivity
• Contribute to Android architecture, shared platform components, design systems, and reusable libraries
• Help evolve our engineering practices around code quality, testing, maintainability, scalability, and release confidence
• Debug complex production issues and use data, logging, and experimentation to guide improvements
• Mentor engineers, provide thoughtful code reviews, and help foster a collaborative, inclusive, and high-performing team culture

Senior Android Engineer

• Design and lead test strategies for streaming workflows, playback systems, and AI-powered features.
• Test across platforms (web, mobile, and connected TV) to ensure functional parity and playback stability.
• Validate streaming performance—including ABR logic, encoding pipelines, and DRM integrations—under diverse real-world conditions.
• Debug with precision using tools like Charles Proxy, Chrome DevTools, ADB, and Xcode. Collaborate with data and ML teams to validate AI model updates, recommendations, and personalization accuracy.
• Leverage AI-assisted QA tools to enhance regression coverage, UI validation, and anomaly detection.
• Contribute to automation and CI/CD frameworks, driving faster, more reliable releases.
• Oversee QA deliverables for multiple concurrent releases and ensure seamless sign-off for production launches.
• Monitor live environments for playback or recommendation anomalies post-release and escalate issues promptly.
• Continuously improve QA processes, metrics, and reporting for streaming and AI validation.

• Lead the design, development, and implementation of advanced recommendation systems and algorithms for a global audience
• Conduct deep dives into algorithmic components and systems, ensuring that models are optimized for both performance and scalability across multiple regions and product areas
• Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment
• Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and provide high-quality user experiences.
• Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement

Machine Learning Engineer – Staff, Principal

• Lead and manage high-performing teams across ML engineering and ML infrastructure, fostering a culture of innovation, collaboration, and growth.
• Define and execute the strategic roadmap for ML systems, including recommendation, personalization, and ads optimization.
• Oversee the design, development, and deployment of scalable ML pipelines: data ingestion, feature engineering, model training, evaluation, and serving.
• Architect distributed systems to support ML workloads at scale, ensuring reliability, observability, and operational excellence.
• Partner closely with Product, Engineering, and Content teams to align on business goals and deliver impactful ML-driven experiences.
• Support best practices in experimentation, evaluation, and ML system monitoring.
• Ensure cost efficiency, scalability, and performance in ML infrastructure investments.