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Added 33 mins ago
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Full-time
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Full-time
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Full-time
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Job Title: Software Development Engineer in Test (SDET)Job Description
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NBCUniversal

ML Solutions Architect - Architecte de solutions ML

Avenue du Parc, Montreal, QC, H2V 4H5, Canada

Job type: Full-time
Posting language: English · French
Posted: Sep 3, 2026

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Applications open in a new tab.

Original job description

English section of this bilingual listing.

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.

Job Description

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 handed off to 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 to spec out 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.

Responsabilités principales

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 models daily, you must have enough ML-adjacent experience to understand model constraints, data requirements, and the "state of the art."
Experience with fine-tuning and deploying models is a plus.
Attributes:
Strategic Decision-Making: Ability to perform "Build vs. Buy" analyses for ML components.
Communication: Exceptional ability to translate high-level product vision into concrete engineering specs for both technical leadership and specialized engineers.
Conscientiousness: High attention to detail regarding system stability and interoperability

Additional Information

As part of our selection process, external candidates may be required to attend an in-person interview with an NBCUniversal employee at one of our locations prior to a hiring decision. NBCUniversal's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law.

If you are a qualified individual with a disability or a disabled veteran and require support throughout the application and/or recruitment process as a result of your disability, you have the right to request a reasonable accommodation. You can submit your request to [email protected].

Business Segment: NBCU Corporate

Read the full bilingual original

Company Description

NBCUniversal est l’un des leaders mondiaux du secteur des médias et du divertissement. Nous créons des contenus d’exception, que nous diffusons à travers notre portefeuille de films, de programmes télévisés et de services de streaming, et que nous donnons vie grâce à nos parcs à thème internationaux, nos produits grand public et nos expériences. Nous détenons et exploitons des marques de premier plan dans les domaines du divertissement et de l’information, notamment NBC, NBC News, NBC Sports, Telemundo, les chaînes locales NBC, Bravo et Peacock, notre service de streaming premium financé par la publicité. Nous produisons et distribuons des films et des programmes de divertissement de premier ordre grâce à nos puissants studios de cinéma et de télévision, notamment Universal Pictures, DreamWorks Animation et Focus Features, ainsi qu’aux quatre studios de télévision mondiaux regroupés sous la bannière Universal Studio Group. Nous exploitons également des parcs à thème et des expériences de premier plan à travers le monde via Universal Destinations & Experiences, notamment l’Universal Orlando Resort, qui abrite l’Universal Epic Universe, et Universal Studios Hollywood. NBCUniversal est une filiale de Comcast Corporation. Rendez-vous sur www.nbcuniversal.com pour plus d’informations.

Notre impact repose sur l’amélioration des communautés dans lesquelles vivent et travaillent nos employés, nos clients et nos publics. Nous avons une riche tradition d’engagement social et veillons à ce que nos employés aient la possibilité de s’investir au sein de leurs communautés. Nous défendons une culture inclusive et nous nous efforçons d’attirer et de former une main-d’œuvre talentueuse afin de créer et de proposer un large éventail de contenus reflétant notre monde.

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, DreamWorks 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 www.nbcuniversal.com for more information.

Job Description

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

Nous sommes à la recherche d’un(e) architecte de solutions ML possédant une solide expertise générale en ingénierie logicielle ainsi qu’une bonne expérience connexe à l’apprentissage automatique. Dans ce rôle, vous serez responsable de la conception de haut niveau des systèmes qui intègrent les modèles ML à notre offre de produits. Vous agirez comme conseiller(ère) technique en évaluant les besoins des clients afin de déterminer s’ils peuvent être satisfaits à l’aide de solutions prêtes à l’emploi ou s’ils doivent être confiés à nos équipes spécialisées en apprentissage profond et en apprentissage par renforcement.

Ce poste convient particulièrement à une personne ayant une vision systémique, capable de traduire une vision produit en une architecture évolutive, tout en conciliant faisabilité technique, performance et maintenabilité.

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 handed off to 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 to spec out 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.

Responsabilités principales

Intégration de systèmes et coprocessing : Concevoir et mettre en œuvre les couches logicielles permettant aux modèles ML d’interagir avec un moteur de rendu en temps réel, y compris les composants de prétraitement et de post-traitement nécessaires à une exécution performante.

Conseil technique : Évaluer les demandes clients et déterminer la meilleure approche à adopter, qu’il s’agisse d’outils disponibles sur le marché ou de projets de recherche spécialisés à confier aux équipes d’apprentissage profond ou d’apprentissage par renforcement.

Ingénierie agnostique aux langages : Développer et maintenir des wrappers, API et microservices afin de garantir la flexibilité et l’interopérabilité de la pile ML dans différents environnements de production et langages de programmation.

Collaboration interfonctionnelle : Agir comme principal point de contact technique entre la direction, les clients et les équipes d’ingénierie afin de définir les exigences d’intégration, de système et de données.

Exécution modulaire : Décomposer des visions produit complexes en composantes architecturales claires afin de livrer des fonctionnalités ML au sein d’un produit logiciel stable et évolutif.

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 models daily, you must have enough ML-adjacent experience to understand model constraints, data requirements, and the "state of the art."
Experience with fine-tuning and deploying models is a plus.
Attributes:
Strategic Decision-Making: Ability to perform "Build vs. Buy" analyses for ML components.
Communication: Exceptional ability to translate high-level product vision into concrete engineering specs for both technical leadership and specialized engineers.
Conscientiousness: High attention to detail regarding system stability and interoperability

Qualifications

Diplôme en informatique, en génie logiciel ou dans un domaine connexe.
Expérience démontrée en tant qu’architecte logiciel, architecte de solutions ou ingénieur(e) système dans un environnement dynamique.
Une expérience dans des secteurs multidisciplinaires tels que la robotique, les réseaux intelligents, l’agriculture de précision, le développement de jeux vidéo ou l’aérospatiale est fortement valorisée.
Compétences techniques
Outillage généraliste : Excellente maîtrise de Git et des environnements Unix, avec la capacité de travailler dans plusieurs langages de programmation selon les besoins, idéalement Python, C++ ou C#.
Connaissances architecturales : Compréhension approfondie de l’intégration de modèles ML dans des logiciels de production, incluant la conception d’API, les courtiers de messages, la conteneurisation ainsi que la planification des ressources de calcul et de mémoire.
Littératie ML : Expérience connexe suffisante en apprentissage automatique pour comprendre les contraintes des modèles, les besoins en données et l’état de l’art, même sans entraîner des modèles au quotidien.
Atout : Une expérience en ajustement fin (fine-tuning) et en déploiement de modèles d’apprentissage automatique constitue un avantage important.
Qualités recherchées
Prise de décision stratégique : Capacité à réaliser des analyses pertinentes de type build vs. buy pour les composants et systèmes intégrant du ML.
Communication : Excellente capacité à traduire une vision produit de haut niveau en spécifications d’ingénierie concrètes pour la direction et les équipes techniques spécialisées.
Rigueur : Grande attention accordée à la stabilité des systèmes, à l’interopérabilité et à la maintenabilité à long terme.

Additional Information

Business Segment: NBCU Corporate

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More about this job

Responsibilities

The ML Solutions Architect will lead the high-level design of systems integrating machine learning models into the product suite. They will act as a technical consultant to evaluate requirements and ensure seamless integration between ML components and real-time rendering engines.

Requirements

Candidates must hold a degree in Computer Science, Software Engineering, or a related field with proven experience as a Software Architect or Systems Engineer. Strong proficiency in Git, Unix, and programming languages like Python, C++, or C# is required, along with deep knowledge of production-level ML integration.

Skills

Machine Learning
Software Architecture
System Design
Python
C++
C#
Git
Unix
API Design
Containerization
Message Brokers
Deep Learning
Reinforcement Learning
Technical Consulting
Data Pre-processing

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Software Architecture
Systems Engineering
Python
C++
C#
Git
Unix
API
Microservices
Containerization
Deep Learning
Reinforcement Learning
Data Processing
Real-time Rendering
Build vs Buy Analysis
Scalability
Interoperability
Technical Consulting
Software Development

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