ML Solutions Architect - Architecte de solutions ML

• Full-time
• Business Segment: NBCU Corporate

NBCUniversal is one of the world's leading media and entertainment companies. We create world-class content, which we distribute across our portfolio of film, television, and streaming, and bring to life through our global theme park destinations, consumer products, and experiences. We own and operate leading entertainment and news brands, including NBC, NBC News, NBC Sports, Telemundo, NBC Local Stations, Bravo, and Peacock, our premium ad-supported streaming service.

We produce and distribute premier filmed entertainment and programming through our powerhouse film and television studios, including Universal Pictures, Dream Works Animation, and Focus Features, and the four global television studios under the Universal Studio Group banner, and operate industry-leading theme parks and experiences around the world through Universal Destinations & Experiences, including Universal Orlando Resort, home to Universal Epic Universe, and Universal Studios Hollywood.

NBCUniversal is a subsidiary of Comcast Corporation. Visit  for more information.

Our impact is rooted in improving the communities where our employees, customers, and audiences live and work. We have a rich tradition of giving back and ensuring our employees have the opportunity to serve their communities. We champion an inclusive culture and strive to attract and develop a talented workforce to create and deliver a wide range of content reflecting our world.

We are seeking an ML Solutions Architect who brings broad software engineering expertise along with strong machine learning-adjacent experience. In this role, you will lead the high-level design of systems that integrate ML models into our broader product suite. You will act as a technical consultant, evaluating customer requirements and determining whether they can be addressed with off-the-shelf solutions or should be escalated as specialized research initiatives for our Deep Learning and Reinforcement Learning teams.

This role is ideal for a systems-minded engineer who can translate product vision into scalable architecture, while balancing technical feasibility, performance, and maintainability.

Key Responsibilities

System Integration & Coprocessing:
Design and implement the software layers that allow ML models to interact with a real-time rendering engine. This includes managing data pre-processing and post-processing (coprocessing) to ensure high-performance execution.

Technical Consulting:
Evaluate incoming customer requirements to determine the optimal path forward. You will decide if a task can be solved using off-the-shelf tools or if it requires a deep-dive research project to be handedoffto our Deep Learning or Reinforcement Learning engineers.

Language-Agnostic Engineering:
Build and maintain wrappers, APIs, and microservices that allow our ML stack to remain flexible and language-agnostic across different production environments.

Cross-Functional Coordination:
Act as the primary technical liaison between technical leadership, customers, and the core engineering team tospecout data and integration requirements.

Modular Execution:
Break down complex product visions into manageable architectural components, ensuring that ML components ship as part of a stable, scalable software product.

Qualifications

Education:

Degree in Computer Science, Software Engineering, or a related field.

Professional

Experience:

Proven experience as a Software Architect or Systems Engineer in a fast-paced environment.

Industry Context:
Prior experience in industries with complex multi-disciplinary teams such as robotics, smart grids, precision agriculture, game development, or aerospace.

Technical Proficiency:

Generalist Tooling:
Fluency with Git, and the Unix shell, with a strong ability to work across multiple programming languages as needed (ideally including one or more of Python, C++, C#).

Architectural Knowledge:
Deep understanding of how to integrate ML models into production software (e.g., API design, message brokers, and containerization, compute and memory budgeting).

ML Literacy:
While you may not be training…