This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Desenvolver e operacionalizar modelos de Machine Learning, garantindo a implementação e manutenção de pipelines eficientes. - Realizar deploy de modelos e gerenciar o ciclo de vida de modelos (LCM) dentro da plataforma GenPlat. - Colaborar com a equipe para fomentar uma cultura forte de IA e MLOps, promovendo práticas de inovação e melhoria contínua. - Apoiar a implementação de agentes e ferramentas baseadas em GenAI, contribuindo para a estabilidade técnica e organizacional. - Participar ativamente no planejamento e execução de projetos, garantindo alinhamento com os objetivos estratégicos da empresa. Qualifications - Experiência em MLOps e deploy de modelos, com sólida base em engenharia de software. - Conhecimento em infraestrutura em nuvem, preferencialmente AWS, e operação de pipelines. - Familiaridade com soluções open-source e vivência em ambientes tech-first. - Capacidade de comunicação clara e colaboração eficaz em equipes multidisciplinares. - Mentalidade voltada para inovação e adaptação a mudanças rápidas. Requirements - Conhecimentos em GenAI e sua aplicação prática em projetos. - Habilidade para liderar iniciativas técnicas e fomentar um ambiente de aprendizado contínuo. - Experiência em aumentar a eficiência no gerenciamento do ciclo de vida de modelos. - Capacidade de se adaptar rapidamente a novas tecnologias e desafios complexos. - Inglês avançado.

• Work mainly with existing open-source libraries, such as Transformers and Datasets to boost the support for vision or multi-modal models and datasets. • Foster one of the most active machine learning communities, helping users contribute to and use the tools that you build. • Interact with Researchers, ML practitioners, and data scientists on a daily basis through GitHub, our forums, or slack.

At Hugging Face, we're on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 11 million users who collectively shared over 2M models, 700k datasets & 600k apps. Our open-source libraries have more than 600k+ stars on Github. About the Role As an Open-Source ML engineer in Computer Vision, you will work mainly with existing open-source libraries, such as Transformers and Datasets to boost the support for vision or multi-modal models and datasets. You will bring your computer vision expertise to provide the best computer-vision tool stack in the machine learning ecosystem and work with us to provide the best, simplest, and most intuitive computer-vision library in the industry. You'll get to foster one of the most active machine learning communities, helping users contribute to and use the tools that you build. You'll interact with Researchers, ML practitioners, and data scientists on a daily basis through GitHub, our forums, or slack. About you - Deep expertise in computer vision: object detection, segmentation, generative models, or multimodal systems. - Strong open-source presence: You’ve contributed significantly to CV libraries (e.g., OpenCV, Detectron2, MMDetection, or Hugging Face’s own transformers/diffusers), as a Core-Contributor or maintainer. - Scalability mindset: Experience optimizing models for production, deploying at scale, or improving inference efficiency. - Collaboration & mentorship: You enjoy working with cross-functional teams, reviewing PRs, and guiding junior contributors. - Alignment with our mission: You believe in democratizing AI and want to empower millions of builders with state-of-the-art tools. If you love open-source, are passionate about the new development of Transformers models in computer vision, have experience building, optimizing, and training such models in PyTorch and/or TensorFlow, serving them in production, and want to contribute to one of the fastest-growing ML libraries, then we can't wait to see your application! If you're interested in joining us, but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and backgrounds complement one another. We're happy to consider where you might be able to make the biggest impact. More about Hugging Face We are actively working to build a culture that values diversity, equity, and inclusivity. We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We value development. You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to grow continuously. We provide all employees with reimbursement for relevant conferences, training, and education. We care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off. We support our employees wherever they are. While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed. We want our teammates to be shareholders. All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside. We support the community. We believe major scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint. • Reinforcement Learning for Data Discovery: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable. • Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions. • Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components. • Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence. • Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.