Senior MLOps Engineer
As a Senior MLOps Engineer, the responsibilities include leading technical scoping and architectural decisions for high-impact ML systems, designing, building, and deploying production-grade ML software, tools, and scalable infrastructure, and defining and implementing best practices and standards for deploying machine learning at scale across the business. The role also involves collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities, acting as a trusted technical advisor to customers and partners by translating complex concepts into actionable strategies, and mentoring and developing junior engineers while actively shaping the team's engineering culture and technical depth.
Senior Python Engineer
As a Senior Python Engineer, the role involves leading the development and deployment of advanced AI systems for diverse clients, designing, building, and deploying scalable, production-grade machine learning software and infrastructure that adhere to strict operational and ethical standards. Responsibilities include leading technical scoping and architectural decisions for high-impact machine learning systems, defining and implementing best practices and standards for deploying machine learning at scale, collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges, acting as a trusted technical advisor to clients by translating complex concepts into actionable strategies, and mentoring junior engineers while contributing to the team's engineering culture and technical depth.
MLOps Engineer
Building and deploying production-grade ML software, tools, and infrastructure; creating reusable, scalable solutions to accelerate the delivery of ML systems; collaborating with engineers, data scientists, and commercial leads to solve critical client challenges; leading technical scoping and architectural decisions to ensure project feasibility and impact; defining and implementing Faculty’s standards for deploying machine learning at scale; acting as a technical advisor to customers and partners by translating complex ML concepts for stakeholders.
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 - Casablanca, Morocco
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 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 - Guadalajara, Mexico
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 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 the highest priority issues.
Tech Lead, Android Core Product - Cebu, Philippines
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 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 - Alexandria, Egypt
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 us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues.
Tech Lead, Android Core Product - Nairobi, Kenya
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for various use cases; deploy and operate 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.
Tech Lead, Android Core Product - Seongnam, South Korea
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 architectures to improve the performance, latency, throughput, and efficiency of deployed models. Build tools for visibility into bottlenecks and sources of instability and design and implement solutions to address high priority issues.
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