Senior Director, Technical Product Management - ML Platform & Infrastructure

Paramount•San Francisco, CA

About The Position

The Applied Machine Learning Group creates personalized streaming experiences that transform how audiences find, interact with, and enjoy content on Paramount+ and Pluto TV. We offer real-time discovery and personalized participation, and improve viewing experiences with advanced short-form and interactive content. This is possible because of our deep audience insights, understanding of different content types, and advanced machine learning. Our work directly connects millions of viewers to the stories they love while leading meaningful business impact for Paramount Streaming. 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, provides content intelligence, and 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, including 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.

Requirements

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, including distributed AI infrastructure, data platforms, or developer platforms, in industries such as 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, feature stores, data consistency, scalability, reliability, and cost optimization.
Experience with MLOps platforms, orchestration, CI/CD for ML, model observability, drift detection, and safe deployment practices.
Fluency with data analysis workflows, including SQL, Python, telemetry, experimentation data, and platform health metrics.
Executive communication skills, with the ability to translate technical platform work into business impact, strategic tradeoffs, and clear decision-making.
Proven ability to align engineering, data science, infrastructure, product, and business stakeholders across matrixed organizations.

Nice To Haves

Experience in building or improving machine learning platforms, including skills in personalization, recommendations, search, content intelligence, advertising, or other large consumer AI systems.
Experience with real-time personalization systems, online feature serving, model serving infrastructure, experimentation platforms, and decisioning systems.
Understanding of foundation model infrastructure and multimodal AI systems, including experience with vector pipelines, embeddings, and new AI application architectures.
Experience balancing platform reliability, developer velocity, infrastructure cost, and product impact at global scale.
Hands-on technical fluency with coding tools such as Python, SQL, IDEs, GitHub, notebooks, dashboards, and developer workflows.
Experience using AI-assisted development tools such as Claude Code, Gemini for developers, Cursor, or similar tools to accelerate prototyping and technical investigation.
Ability to individually create, evaluate, or demonstrate basic proofs of concept for ML platform, recommendation, personalization, or AI infrastructure ideas.
Understanding of how internal developer platforms function, with a focus on adoption and usability, promoting self-service workflows, and ensuring clear documentation and design of platform APIs.
Affinity for building the technical foundations that power the future of streaming, personalization, and AI-driven entertainment.

Responsibilities

Define and lead the strategy for our ML platform and infrastructure, spanning multiple years, to 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, including real-time inference, 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.
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.
Improve developer productivity 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, content intelligence, search, short-form content, experimentation, and future AI-powered product experiences.
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, including 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.
Define and track north-star metrics for ML platform success, including model deployment velocity, system reliability, latency, availability, infrastructure cost efficiency, developer productivity, platform adoption, and time from model development to production impact.
Ensure good visibility, focus on safety during experiments, monitor models, and maintain 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.
Stay at the forefront of large-scale machine learning platforms, focusing on MLOps, real-time inference, and distributed model serving, and 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.