AI Engineer
As an AI engineer at Maki, you will build AI features by designing and implementing end-to-end AI functionalities that power Maki’s HR agents from prototyping to production. You will experiment with large language models (LLMs) by testing, fine-tuning, and evaluating cutting-edge models to balance performance, cost, and safety. Additionally, you will develop evaluation pipelines to measure accuracy, reliability, bias, and business impact of AI-driven features. Your work will include optimizing AI features for scale through latency, caching, and cost-optimization strategies to make them enterprise-ready, as well as ensuring safety and compliance by collaborating with security and product teams to meet enterprise standards for privacy, fairness, and reliability. You will also collaborate cross-functionally with product managers, designers, and engineers to turn ideas into shipped features and stay ahead by exploring new AI research, tools, and frameworks to keep Maki at the cutting edge.
AI Deployment Engineer
As an AI Deployment Engineer, you will serve as the primary technical subject matter expert post-sale for a portfolio of customers, embedding deeply with them to design and deploy Generative AI solutions. You will engage with senior business and technical stakeholders to identify, prioritize, and validate the highest-value GenAI applications in their roadmap. Your role includes accelerating customer time to value by providing architectural guidance, building hands-on prototypes, and advising on best practices for scaling solutions in production. You will maintain strong relationships with leadership and technical teams to drive adoption, expansion, and successful outcomes. Additionally, you will contribute to open-source resources and enterprise-facing technical documentation to scale best practices across customers, share learnings and collaborate with internal teams to inform product development and improve customer outcomes, and codify knowledge and operationalize technical success practices to help the Solutions Architecture team scale impact across industries and customer types.
Senior ML Operations (MLOps) Engineer
As a Senior ML Operations Engineer at Eight Sleep, you will pioneer cutting-edge ML technologies and integrate them into products and processes for health monitoring. You will own the design and operation of robust ML infrastructure by building scalable data, model, and deployment pipelines to ensure reliable model delivery to production. Your role involves partnering cross-functionally with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales across Pods globally. You will optimize ML systems for cost-effectiveness, scalability, and high performance by managing compute, storage, and deployment resources during training and inference. Additionally, you will develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment, and maintain clear and direct communication within a remote work environment.
Tech Lead, Android Core Product - Rennes, France
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues.
Tech Lead, Android Core Product - Strasbourg, France
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the 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.
Tech Lead, Android Core Product - Montpellier, France
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 that improve the 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.
Tech Lead, Android Core Product - Lille, France
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for various use cases. Deploy and operate the core machine learning 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 gain visibility into bottlenecks and sources of instability, then design and implement solutions to address the highest priority issues.
Tech Lead, Android Core Product - Bordeaux, France
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Tech Lead, Android Core Product - Grenoble, France
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to their customers for a diverse range of use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that 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 the highest priority issues.
Tech Lead, Android Core Product - Nantes, France
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability, then design and implement solutions to address the highest priority issues.
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