Company Description 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. 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. 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. Job Description We are seeking a Reinforcement Learning Engineer with experience manipulating virtual environments to train autonomous agents. This role focuses on the design of robust simulation environments, reward structures, and policy architectures that can navigate complex, multi-sensor landscapes. Key Responsibilities - Cross-Functional Coordination: Work with partner ML and Annotation engineers and TPMs to spec out data, simulation, and training requirements. - Environment Design: Build and maintain high-fidelity 2D/3D simulation environments (using tools like Unity, Unreal, or Isaac Sim) that serve as the training ground for RL agents. - Reward Engineering: Design and tune complex reward functions that align agent behavior with product goals and safety constraints. - Algorithm Implementation: Develop and optimize RL algorithms (e.g., PPO, SAC, or Offline RL) capable of handling high-dimensional 3D observation spaces. - Sim-to-Real Strategy: Analyze the "reality gap" and implement domain randomization or adaptation techniques to ensure models perform reliably in real-world scenarios. Nous sommes à la recherche d'un(e) ingénieur(e) en apprentissage par renforcement ayant de l'expérience dans la création et l'exploitation d'environnements virtuels pour l'entraînement d'agents autonomes. Ce rôle consiste à concevoir des environnements de simulation robustes, des structures de récompense et des architectures de politiques capables d'évoluer dans des contextes complexes et multi-capteurs. Vous jouerez un rôle clé dans le rapprochement entre simulation et performance réelle en développant des systèmes RL évolutifs et en garantissant un comportement fiable des agents dans des conditions variées. - Collaboration interfonctionnelle : Travailler avec les ingénieurs ML, les équipes d'annotation et les TPM afin de définir les besoins en données, en simulation et en entraînement. - Conception d'environnements : Développer et maintenir des environnements de simulation 2D/3D à haute fidélité à l'aide d'outils tels que Unity, Unreal ou Isaac Sim. - Ingénierie des récompenses : Concevoir et optimiser des fonctions de récompense afin d'aligner le comportement des agents avec les objectifs produit et les contraintes de sécurité. - Implémentation d'algorithmes : Développer et optimiser des algorithmes d'apprentissage par renforcement (ex. : PPO, SAC, RL hors ligne) adaptés à des espaces d'observation à haute dimension. - Stratégie sim-to-real : Réduire l'écart entre simulation et réalité à l'aide de techniques comme la randomisation de domaine et l'adaptation afin d'assurer des performances fiables en conditions réelles. Qualifications - Education: Graduate degree (Master's or PhD) in Robotics, Computer Science, AI, or a related field with a focus on Reinforcement Learning, Imitation Learning, or other Online Machine Learning fields. - Professional Experience: Proven experience as an RL Engineer or Research 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: - Core Tools: Fluency with Python, Git, and the Unix shell. - RL Frameworks: Deep familiarity with frameworks like Ray Rllib, Stable Baselines3, or CleanRL. - Physics & 3D Engines: Experience with physics engines (MuJoCo, Bullet) or 3D game engines. - Ecosystem: Familiarity with collaborative tools such as Jira/Confluence, Slack, a Git server, and an experiment tracking framework. Attributes: - Strong Mathematical Background: Essential for understanding Markov Decision Processes (MDPs) and gradient-based optimization. - High Attention to Detail: Critical for debugging non-deterministic agent behaviors and ensuring environment parity. - Formation : Maîtrise ou Doctorat en robotique, informatique, intelligence artificielle ou domaine connexe avec une spécialisation en apprentissage par renforcement, imitation ou apprentissage en ligne. - Expérience : Expérience démontrée en tant qu'ingénieur(e) en apprentissage par renforcement ou en recherche dans un environnement dynamique. - Contexte industriel : Une expérience dans des secteurs multidisciplinaires tels que la robotique, les réseaux intelligents, l'agriculture de précision, les jeux vidéo ou l'aérospatiale est fortement valorisée. Compétences techniques - Outils principaux : Excellente maîtrise de Python, Git et des environnements Unix. - Frameworks RL : Expérience avec des frameworks tels que Ray RLlib, Stable Baselines3 ou CleanRL. - Physique et simulation : Expérience avec des moteurs physiques (MuJoCo, Bullet) ou des environnements de simulation 3D. - Écosystème : Familiarité avec des outils collaboratifs tels que Jira, Confluence, Slack, les workflows Git et les plateformes de suivi d'expériences. Qualités recherchées - Solides bases mathématiques : Bonne compréhension des processus de décision de Markov (MDP) et de l'optimisation basée sur le gradient. - Rigueur et précision : Capacité à déboguer des systèmes non déterministes et à assurer la cohérence et la précision des environnements de simulation. 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 AccessibilitySupport@nbcuni.com.

• Partner with Product, Engineering, Clinical, Operations, Marketing, and Data Engineering to design, build, deploy, and operate scalable machine learning and AI systems. • Own the end-to-end machine learning lifecycle: data and feature engineering through deployment, monitoring, experimentation, and continuous improvement. • Build production ready time series models to predict real time KPIs and optimize decision actions for clinical operations business optimization. • Propose, evaluate and interpret results for clinical, product and business decision-makers and own outcomes. • Collaborate closely with peers and stakeholders to distill requirements of problem definitions, product features, and architecture to improve clinical outcomes using insights and models. • Develop modular, well-tested, production-quality software using Python, Spark and SQL to build scalable data engineering and machine learning pipelines following best practices. • Ensure robust model lifecycle management through model versioning, MLflow, automated testing, CI/CD, and production monitoring. • Build and optimize scalable Spark and Databricks workloads, leveraging distributed computing best practices for large-scale data processing and real-time inference. • Monitor production models and data pipelines for data quality, feature drift, concept drift, latency, reliability, and business performance, proactively identifying and resolving issues.

• Research and develop image-processing and computer-vision algorithms for 2D and 3D scene segmentation. • Develop effective experiments and methods to detect and characterize model performance issues in long-tail conditions. • Exploring Deep Learning models and/or frameworks to push the performance frontiers of our current system, including exploring methods in the active learning space.

Role Description Penguin Random House is the largest trade publishing company in the world. The Data Science team is seeking a Staff Machine Learning Scientist to lead and advance the development of personalization products, including recommender systems for our websites, email programs, and online marketing. Personalization is a core growth lever for book discovery and customer engagement, directly improving how readers find the right books across every digital touchpoint. Improving recommendation quality and relevance has a direct downstream impact on customer experience and business outcomes. We are investing in expanding our portfolio of business-critical personalization products and further improving our existing models. This role will own personalization and recommender system work end-to-end, from model development to deployment to output monitoring, in close partnership with business stakeholders, platform engineers, and the rest of the personalization group. We have a mature machine learning practice and strong infrastructure, supported by strong data warehouse and DevOps partners. We are transitioning to AI-accelerated development and use modern agentic coding tools like Claude Code to speed up how we build and maintain personalization systems, with rigorous quality gates including tests, reproducible workflows, and measurable improvements in model performance and reliability. Experience with Claude Code or agentic workflows is a plus, but we prioritize strong fundamentals and the ability and willingness to learn new workflows effectively. - Define and drive the technical roadmap for personalization and recommender systems, prioritizing roadmap items to meet business goals and defining short-term vision for the team. - Propose and deliver R&D that directly shapes roadmaps, multiple projects, and long-term deliverables. Models are used over the long term by multiple products and teams. - Design and lead the development of software used by multiple teams, ensuring long-term maintainability, scalability, and adaptability. - Ensure complex, multi-service personalization products meet SLAs and provide correct results over time. Adapt systems to changing business needs and resolve multi-product, multi-team service incidents. - Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces. - Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects. - Manage stakeholders' expectations with data-driven narratives and communicate effectively with senior leadership to align on strategy and track progress. - Drive organizational efficiency and business impact by implementing new technologies and processes. Foster a collaborative and high-performance team culture. - Mentor senior and mid-level scientists, setting high code quality standards and best practices for the team. - Stay current with advances in recommender systems, LLMs for personalization, and representation learning, bringing relevant advances into production when they deliver measurable improvement. Qualifications - PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related quantitative field, OR Master's with 8+ years of applied ML experience. - Deep expertise in recommender systems, personalization, ranking/retrieval, or computational advertising, with a track record of shipping systems that operate at scale. - Expert-level Python and deep proficiency with modern ML frameworks (PyTorch or TensorFlow) and recommendation-specific tooling (e.g., NVTabular, Merlin, Triton). - Strong experience with cloud-based ML infrastructure (AWS, Kubernetes, Databricks), containerization (Docker), and model serving at low latency. - Advanced SQL skills and experience architecting large-scale data pipelines and feature stores. - Demonstrated ability to define technical roadmaps, influence direction across teams, and make architectural decisions that hold up over time. - Excellent communication skills with the ability to present complex technical work to executive and non-technical audiences. - Be cutting edge. Use the latest AI tools to develop well-designed and robust software. Requirements - Experience building and scaling real-time recommendation services handling millions of requests. - Expertise in A/B testing methodology, causal inference, or experimentation platforms. - Familiarity with LLM-based approaches to recommendation and content understanding. - Experience with MLOps practices: model monitoring, feature stores, CI/CD for ML, and automated retraining pipelines. - Prior experience technically leading a team of ML practitioners and setting standards adopted by others. Benefits - Medical/Prescription drug insurance - Dental - Vision - Health Care/Dependent Care Flexible Spending Account - Health Savings Account - Pre-Tax and Roth 401(k) - Short and Long-Term Disability Insurance - Life/AD&D Insurance - Commuter Benefits - Student Loan Repayment Program - Educational Assistance - Generous paid time off