Software Engineer, macOS Core Product - Campina Grande, Brazil
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability, then design and implement solutions to address high priority issues.
Software Engineer, macOS Core Product - Recife, Brazil
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases; deploy and operate the core ML inference workloads for AI Voices serving pipeline; introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models; build tools to gain visibility into bottlenecks and sources of instability and design and implement solutions to address highest priority issues.
Software Engineer, macOS Core Product - Barueri, Brazil
Work alongside machine learning researchers, engineers, and product managers to bring our AI Voices to their customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for our AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our deployed models. Build tools to give visibility into bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - Natal, Brazil
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Enterprise Account Executive - Italy
The AI Outcomes Manager will partner with executive sponsors and end users to identify high-impact use cases and turn them into measurable business outcomes on Glean. They will lead strategic reviews and advise customers on their AI roadmap to ensure maximum value from Glean's platform. The role involves translating business needs into clear problem statements, success metrics, and practical AI solutions while collaborating with Product and R&D to shape priorities. They will conduct discovery workshops, scope pilots, and guide rollouts to drive broad and deep adoption of the Glean platform. Additionally, they will design and build AI agents with and for customers, including rethinking and redesigning underlying business processes to maximize impact and usability. The manager will proactively identify expansion opportunities and drive engagement across teams and functions.
Senior AI Engineer - San Mateo, CA
The role involves training, evaluating, and monitoring new and improved LLMs and other algorithmic models. The engineer will test and deploy content moderation models in production and iterate based on real-world performance metrics and feedback loops. They are expected to develop medium to long-term vision for content understanding-related R&D, collaborating with management, product, policy & operations, and engineering teams. The position requires taking ownership of results delivered to customers, advocating for changes in approach where needed, and leading cross-functional execution.
MCP & Tools Python Developer - Agent Evaluation Infrastructure
Developing and maintaining MCP-compatible evaluation servers, implementing logic to check agent actions against scenario definitions, creating or extending tools that writers and QAs use to test agents, working closely with infrastructure engineers to ensure compatibility, and occasionally helping with test writing or debug sessions when needed.
Customer Success Solution Architect (Brazil)
The Solution Architect is responsible for developing detailed and scalable architectural designs to address client needs using Articul8 products and third-party libraries and tools. They run pilot programs with customers to demonstrate the feasibility and advantages of proposed solutions, including introducing new product features or building prototypes. The role requires working closely with clients to understand business challenges and technical requirements through workshops, meetings, and presentations. They optimize solutions for performance, reliability, and cost-effectiveness, selecting appropriate instance types, auto-scaling configurations, and storage options. Ensuring solutions comply with security best practices and regulatory requirements is necessary, including implementing identity and access management, data encryption, and other security measures. The architect also creates comprehensive documentation and provides training on solution implementation and management. Collaboration with cross-functional teams such as Applied Research, Engineering, Quality Assurance, and Customer Success is required to incorporate innovation and maintain product leadership. Additionally, the role involves mentoring and guiding junior team members and helping to build a culture of rapid innovation.
Backend Engineer - (Python) Brazil
Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Serve as the subject matter expert in infrastructure for designing new products and introducing new technology to existing product lines. Collaborate closely with engineering and research teams to integrate infrastructure components with product features, ensuring optimal system performance and user experience. Design event-driven architectures and develop APIs and microservices to support real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling mechanisms. Stay updated with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.
Staff AI/Machine Learning Engineer
Act as a technical reference for the team, supporting engineers through design reviews, technical discussions, and hands-on problem-solving. Design, guide, and evolve LLM- and GenAI-based systems such as AI agents, RAG pipelines, and decision-support tools, balancing performance, cost, reliability, and user impact. Influence the architecture and implementation of ML systems across the stack, including data pipelines, experimentation, deployment, and monitoring in production. Define and promote best practices and standards for model evaluation, experimentation, observability, and iteration across ML initiatives. Partner closely with product and engineering teams to shape ML-driven solutions, clarify trade-offs, and ensure alignment with business goals. Lead technically complex or ambiguous initiatives, unblocking teams and driving clarity where requirements or approaches are not well-defined. Improve the maturity of ML infrastructure and workflows to support multiple contributors and use cases over time. Stay current with advancements in GenAI, LLM tooling, and ML systems, and selectively introduce new approaches that provide clear value. Share knowledge through documentation, mentoring, and collaborative problem-solving to raise the technical bar across the organization.
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