Paramount

• Explore, evaluate, and implement new technologies to optimize development efficiency and productivity • Develop technical requirements documentation based on product requirements • Technical development, debugging, and delivery of best-in-class tvOS and iOS native applications • Promote good business practices in a positive and constructive manner • Collaborate successfully with cross-functional teams to define, design, and deploy new features • Work closely with your team to ensure the timely delivery of features and improvements • Manage multiple tasks and projects in a results-driven environment

• Ensure systems align with real-world production workflows, tools, and operational constraints. • Define domain-specific acceptance criteria and quality standards for outputs. • Identify workflow mismatches early and guide teams toward solutions that fit existing production environments. • Validate prototypes and systems within realistic production scenarios, including edge cases and constraints. • Provide actionable feedback on usability, quality, and workflow integration. • Ensure outputs align with established domain standards, including formats, conventions, and downstream requirements. • Contribute directly to the development of new applied ML capabilities through validation scripts, data preparation workflows, evaluation datasets, prototype utilities, model behavior analysis, and integration tooling. • Partner with Applied ML Engineers to translate complex domain requirements into testable specifications, model behavior expectations, prototype workflows, and production-ready system behaviors. • Support evaluation design through representative examples, edge cases, and ground-truth data. • Work with the ML Behavior Systems Team to ensure systems meet defined evaluation and quality standards. • Validate that outputs meet professional expectations within the domain, beyond technical correctness. • Help interpret evaluation results in the context of real production use. • Ensure systems can be adopted within real production pipelines with minimal friction. • Validate compatibility with downstream tools, workflows, and operational environments. • Partner with Platform Integration teams to ensure outputs meet functional deployment requirements.

• Lead applied creative exploration at the intersection of storytelling, production craft, and emerging creative tools and workflows. • Design and execute creative prototypes and proof-of-concept work that test creative value, technical feasibility, and production impact. • Evaluate emerging tools, models, and workflows through hands-on experimentation in real production conditions. • Explore new artistic and production approaches spanning animation, live action, and visual effects. • Partner with studios and business units to deliver proof-of-concept and targeted go-live projects aligned to real production needs. • Tailor creative exploration to the workflows, constraints, and creative environments of adopting teams across animation, live-action, and VFX. • Serve as a creative production partner to studio teams evaluating and piloting new creative capabilities. • Support early adoption by validating workflows in realistic, production-ready contexts. • Work closely with Platform Integration to align creative exploration with identified production opportunities. • Collaborate with Engineering to inform tool design, usability, and creative affordances from a working artist’s perspective. • Translate exploratory ideas into tangible creative demonstrations that can be validated, refined, and handed off. • Build, lead, and mentor small, agile creative production teams optimized for rapid experimentation and delivery. • Assemble and manage project-based artists and specialists as needed to support specific initiatives or creative domains. • Create compelling creative demonstrations that clearly communicate new capabilities and workflows to internal stakeholders.

• Perform rapid diagnosis across model, data, code, infrastructure, and evaluation layers for blocked or unstable efforts. • Identify root causes and define corrective actions required to restore progress. • Communicate findings and resolution plans clearly across research, engineering, and operational teams. • Contribute directly to blocked ML initiatives by implementing fixes across model behavior, data pipelines, and system architecture. • Develop and validate solutions, including debugging, targeted refactoring, and experimental validation. • Ensure that resolved systems are stable, validated, and ready for continued development. • Provide clear handoff artifacts, including working code, documentation, and recommended next steps. • Work across research, infrastructure, platform, evaluation, and integration teams to align on root causes and resolution plans.

• Work with large volumes of traffic data and user behaviors to develop pipelines that enhance raw data quality. • Break down complex data problems and communicate feasible solutions proficiently. • Extract patterns from large datasets and transform data into actionable insights. • Utilize tools such as Jupyter, SQL, dashboards, statistical analysis, and data visualizations to explore datasets and find answers to business questions. • Partner with internal product and business intelligence teams to identify optimal data ingestion, structure, and storage approaches. • Collaborate with technology field partners to ensure reliable implementation of data solutions. • Provide innovative ideas to improve data and collaborate with engineering, BI teams, and business units to implement changes. • Lead projects from inception to completion, ensuring successful implementation of data solutions. • Contribute to advancing the team in technology and best practices. • Mentor junior team members on best practices and approaches around data.

• Own the implementation of features for JBI (Jump Back In) and YNW (Your Next Watch) • Use Qdrant to find high-relevance candidates for re-entry carousels based on session history and global trends • Use MLFlow to manage, track, and deploy experiments, ensuring a high bar for reproducibility • Develop and serve models in GCP using TensorFlow/PyTorch, incorporating Post-training RL for reward-based optimization • Participate in design reviews to ensure your components share the same high-reliability standards as the rest of the pod • Build the logic that makes the app feel personalized even for users we know very little about • Optimize for the 'Play' button—the ultimate signal of user commitment

• Design and refine AI-integrated workflows that support creative production, production management, review, planning, documentation, asset discovery, localization, and operational handoffs. • Architect tools and workflows that incorporate large language models, multimodal AI systems, agents, retrieval workflows, automation, structured data, media-generation systems, and human-in-the-loop review. • Translate user needs, production pain points, and emerging AI capabilities into practical workflow concepts, prototypes, platform patterns, and tool requirements. • Develop repeatable AI workflow patterns that can scale across teams, departments, productions, and divisions. • Balance local runtimes, cloud AI services, enterprise platforms, vendor tools, APIs, and internal production systems as appropriate for each workflow. • Ensure AI-enabled tools are designed around clear user value, creative control, production safety, reliability, and practical adoption. • Build, configure, or guide the development of AI-powered prototypes, workflow experiments, reference implementations, and user-facing tools. • Partner with engineers, product managers, research teams, evaluation partners, and production users to test whether AI-assisted workflows are useful, controllable, repeatable, and understandable. • Create realistic use cases and pilot scenarios that reflect actual production needs, constraints, and edge cases. • Evaluate tools through hands-on testing, user feedback, workflow observation, and production-readiness criteria. • Assess usability, reliability, trust, reviewability, explainability, governance needs, and fit within existing production workflows. • Identify where AI outputs require human review, structured constraints, metadata, permissions, auditability, or additional product safeguards. • Evaluate current-state workflows, identify bottlenecks, manual effort, handoff issues, duplication, information gaps, and friction points.

• Architecting and shipping stakeholder-facing AI workflow solutions • Lead the design for complex integrations • Define and evolve reference patterns for delivery shapes • Conduct readiness reviews for high-impact releases • Standardize reliability patterns across the portfolio • Ensure the quality and consistency of the Solution Bundle • Accountabilities for coherent architecture and standards across integration portfolio • Success of complex/critical integrations and technical risk reduction across brands

• Translate complex business problems into coherent, actionable quantitative solutions. • Implement, automate, and maintain reliable, performant, and end-to-end ML systems using software engineering and MLOps best practices. • Deliver clear, impactful insights to stakeholders through effective communication. • Collaborate with stakeholders across the Product org to operationalize data science solutions that inform strategy and optimize the user experience. • Promote best practices across the data science and product analytics team.

• Explore, evaluate, and implement new technologies to optimize development efficiency and productivity • Develop technical requirements documentation based on product requirements • Technical development, debugging, and delivery of best-in-class tvOS and iOS native applications • Promote good business practices in a positive and constructive manner • Collaborate successfully with cross-functional teams to define, design, and deploy new features • Work closely with your team to ensure the timely delivery of features and improvements