Senior Director, Technical Product Management for ML Platform & Infrastructure

This is a Full Time Job

#WeAreParamount on a mission to unleash the power of content… you in?

Overview:

#WeAreParamount on a mission to unleash the power of content… you in? We’ve got the brands, we’ve got the stars, we’ve got the power to achieve our mission to entertain the planet – now all we’re missing is… YOU! Becoming a part of Paramount means joining a team of enthusiastic people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Collectively, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture.

We are looking for a Senior Director of Technical Product Management. This person will be in charge of product strategy and execution for our ML platform and infrastructure. The platform allows for personalization. It provides content intelligence. It also offers AI-driven streaming experiences. These features are designed for Paramount's global ecosystem.

This leader will set the vision and plan for key ML platform features. These features include model training, evaluation, lifecycle management, and serving models in both real-time and batch formats. They will also manage feature stores, data pipelines, and ML data platforms. Additionally, they will oversee runtime systems and integrations. Their focus will be on developer tools and ensuring that AI systems are reliable, scalable, low-latency, and cost-efficient.

This is a highly strategic and deeply technical leadership role. You will partner closely with engineering, data science, product, and executive management to ensure our ML platform enables rapid innovation, production-grade reliability, and proficient global scale. The role is responsible not only for how ML systems are built and operated, but for how platform capabilities accelerate the future of AI across Paramount Streaming.

Primary Responsibilities:

Strategy & Vision
• Define and lead the strategy for our ML platform and infrastructure. This plan will span multiple years. It will support personalization, discovery, content intelligence, and new AI-driven streaming experiences.
• Translate company objectives into scalable platform investments with clear reliability, performance, developer velocity, and cost outcomes
• Identify new capabilities for the machine learning platform. This includes real-time inference. It also covers enabling foundation models, multimodal systems, and hybrid AI architectures.
• Represent ML platform strategy at the executive and cross-company level
• Balance long-term platform investment with near-term product acceleration and measurable business impact

Technical Product Leadership
• Lead product strategy for end-to-end ML lifecycle capabilities, including training, deployment, monitoring, iteration, and model governance
• Drive roadmap and prioritization for real-time and batch inference infrastructure
• Lead product direction for feature stores, ML data platforms, data pipelines, and offline/online consistency frameworks
• Enable experimentation platforms, model evaluation workflows, and safe rollout practices for AI and ML systems
• We aim to improve developer productivity. We will do this using platform APIs, tools, abstractions, documentation, and self-service capabilities.
• Partner with ML, platform, data, and cloud engineering teams to build scalable, reliable, secure, and cost-efficient AI systems
• Drive alignment between research innovation, platform capabilities, production readiness, and business outcomes
• Ensure platform decisions improve personalization. They should also aid in content intelligence, search, short-form content, experimentation, and future AI-powered product experiences.

Organizational Leadership
• Lead, mentor, and grow a team of technical product managers
• Establish operating rhythms, product standards, roadmap governance, and clear prioritization frameworks
• Foster a data-driven, experimentation-first culture across product, engineering, data science, and analytics teams
• Influence roadmaps for different teams. These teams include personalization, UX, growth, marketing, content strategy, and platform engineering.
• Build effective partnerships across matrixed global organizations and help teams operate from a shared technical and product strategy

Measurement & Business Impact
• Define and track north-star metrics for ML platform success, including:
• Model deployment velocity
• System reliability, latency, and availability
• Infrastructure cost efficiency
• Developer productivity and platform adoption
• Time from model development to production impact
• Ensure good visibility. Focus on safety during experiments. Monitor the models. Keep high operational standards.
• Translate platform performance, telemetry, and developer feedback into clear product direction
• Communicate technical strategy, tradeoffs, platform health, and business impact clearly to executive stakeholders

Industry Leadership
• Stay at the forefront of large-scale machine learning platforms. Focus on MLOps, real-time inference, and distributed model serving. Work with infrastructure for foundation models, multimodal AI systems, and cloud-native AI architectures.
• Bring external perspective and best practices from leading consumer technology, streaming, cloud, and AI-driven organizations
• Help shape how Paramount Streaming builds, scales, and operates the technical foundations for applied AI

Basic Qualifications
• 10 years of experience in product management, technical product management, or equivalent product executive team roles
• 5 years leading ML platform, infrastructure, MLOps, data platform, or AI-driven systems
• Experience leading PMs or leading complex platform product areas across multiple engineering and science teams
• Experience delivering large-scale machine learning platforms. This includes distributed AI infrastructure, data platforms, or developer platforms. Relevant industries are streaming, consumer technology, cloud platforms, or AI-first companies.
• Technical fluency across ML lifecycle systems, data infrastructure, distributed systems, model serving, and production AI architectures
• Experience with real-time and batch inference architectures. Knowledge of feature stores, data consistency, and scalability. Understand reliability. Understand cost