ML Solutions Architect - Architecte de solutions ML

Nbcuniversal3; Montreal; On-site; CAD 120,000 - 180,000; Full time

Job summary

NBCUniversal seeks an ML Solutions Architect to design systems that integrate ML models into our product suite and lead high-level architecture across data pipelines, APIs, and deployment considerations for real-time rendering environments.

You will act as a technical consultant, evaluating customer requirements and guiding decisions between off-the-shelf solutions and specialized research initiatives for our DL/RL teams, ensuring a scalable, maintainable ML stack.

Qualifications

• Education: Degree in Computer Science, Software Engineering, or a related field.
• Professional experience as Software Architect or Systems Engineer in a fast-paced environment.
• Experience in industries with complex multi-disciplinary teams such as robotics, smart grids, or aerospace.
• Technical proficiency with Git, Unix shell, and multiple programming languages (Python, C++, C#).
• ML-adjacent experience and understanding of model constraints, data requirements, and state-of-the-art techniques.

Responsibilities

• System Integration & Coprocessing: Design and implement software layers that allow ML models to interact with a real-time rendering engine, including data pre-processing and post-processing for high performance.
• Technical Consulting: Evaluate customer requirements to determine whether they can be addressed with off-the-shelf tools or require deep-dive research for DL/RL teams.
• Language-Agnostic Engineering: Build and maintain wrappers, APIs, and microservices enabling a language-agnostic ML stack across production environments.
• Cross-Functional Coordination: Act as the primary technical liaison between leadership, customers, and core engineering to specify data and integration requirements.
• Modular Execution: Break down product visions into architectural components to deliver ML features within a stable, scalable software product.

Skills

Systems thinking

Software architecture

ML literacy

Cross-functional collaboration

Education

Degree in Computer Science or Software Engineering

Tools

Git

Unix shell

Python

C++

C#

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.

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 \"Bui\"