• Implement our messaging protocols in our reference SDK • Own the API design and developer experience • Contribute to messaging protocol specifications • Write clear technical documentation

Role Description As a Labeling Infrastructure Backend Engineer, you will design, build, and scale the mission-critical systems that power our data flywheel. You will be responsible for the high-performance architecture that manages massive datasets, orchestrates complex human-in-the-loop workflows, and integrates seamlessly with our machine learning pipelines. Your work ensures that our labeling operations are fast, reliable, and capable of handling the next order of magnitude in data volume. - Architect Scalable Systems: Design and maintain robust, distributed backend services capable of managing millions of labeling tasks and high-throughput data streams with minimal latency. - Optimize Data Pipelines: Build and refine processes to ingest raw data from diverse sources and deliver high-quality labeled outputs to model training environments. - ML Integration: Implement "Active Learning" and "Model-in-the-loop" features, enabling automated pre-labeling and intelligent task prioritization to maximize human efficiency. - API Development: Develop and document clean, performant APIs that serve as the bridge between our front-end labeling tools, third-party vendors, and internal ML platforms. - Infrastructure & DevOps: Manage cloud-native infrastructure to ensure 99.9+% availability, focusing on observability, automated scaling, and cost-efficiency. - Cross-Functional Collaboration: Partner with AI team and Product Managers to translate complex data requirements into technical specifications and durable backend solutions. Qualifications - Proven track record of building scalable, reliable infrastructure in a fast-paced environment. - Ability to collaborate effectively across teams. - Strong development experience with Python and SQL. - Prior experience with Trino, Flyte / Airflow, and Kubernetes are a plus. - Prior experience with ML Ops workflows is a plus. - Prior experience building and managing data platforms for multimodal ML needs is a plus. - Prior experience with agentic workflows is a plus. - Prior experience in autonomous vehicles (AV) is a plus. Company Description Stack is developing revolutionary AI and advanced autonomous systems designed to enhance safety, reliability, and efficiency of modern operations. Stack's autonomous technology incorporates cutting-edge advancements in artificial intelligence, robotics, machine learning, and cloud technologies, empowering us to create innovative solutions that address the needs and challenges of the dynamic trucking transportation industry. With decades of experience creating and deploying real world systems for demanding environments, the Stack team is dedicated to developing an autonomous solution ecosystem tailored to the trucking industry's unique demands.

• Manage a distributed team of engineers • Facilitate cross-team collaboration • Remove blockers and impediments • Promote engineering best practices • Define product roadmap and ensure alignment

• Vivência com performance e apoio ao desenvolvimento de sistemas; • Criação de sistemas/serviços web nas plataformas; • Metodologias ágeis e DevOps; • Vivência no desenvolvimento de projeto em equipes ágeis; • Elaboração de documentação funcional e manuais de sistema; • Levantamento de Requisitos e análise de necessidades de softwares; • Desenvolvimento e Arquitetura de Sistema utilizando POO, Design-Patterns e princípios SOLID;