ML Infrastructure Engineer Jobs

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The role involves building a platform used by Data Scientists and Simulation Engineers to build, train, and deploy Deep Physics Models. The candidate will work on a focused, stream-aligned, and cross-functional team that includes back-end, front-end, and design members, empowered to make its own implementation decisions towards meeting its objectives. Responsibilities include gathering and leveraging domain knowledge and experience from the Data Scientists and Simulation Engineers using the product, taking ownership of work from implementation to production, ensuring quality, scalability, and observability at every step, which includes testing, containerization, continuous integration and delivery, authentication, authorization, telemetry, observability, and monitoring.

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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.

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Work alongside machine learning researchers, engineers, and product managers to deliver 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 identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse 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 high priority issues.

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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, then design and implement solutions to address the highest priority issues.

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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.

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Develop and deploy LLM agent systems within the AI-powered avatar framework. Design and implement scalable and efficient backend systems to support AI applications. Collaborate with AI and NLP experts to integrate LLM and LLM-based systems and algorithms into the avatar ecosystem. Work with Docker, Kubernetes, and AWS for AI model deployment and scalability. Contribute to code reviews, debugging, and testing to ensure high-quality deliverables. Document work for future reference and improvement.

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Design, train, and fine-tune state-of-the-art language models; develop AI agents combined with retrieval-augmented language models; build efficient and scalable machine learning training and inference systems; stay up-to-date with the latest literature and emerging technologies to solve novel problems; work closely with product and design teams to develop intuitive applications that create societal impact.

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Build, scale, and uphold the technical backbone of a global AI product. Personally build critical AI systems while shaping engineering culture, hiring strategy, and long-term technical direction. Set the technical and cultural foundation of the AI organization. Own AI quality, reliability, and scalability end-to-end. Balance research ambition with real product delivery. Act as the final technical decision-maker. Personally build and maintain core AI infrastructure. Design model training, evaluation, and deployment pipelines. Debug and resolve production AI failures. Review and merge critical PRs. Define standards for model lifecycle and experimentation. Design org structure and hiring strategy. Align AI roadmap with business goals.

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Design and implement end-to-end AI systems, build training, inference, and evaluation pipelines, write production orchestration and feedback code, optimize latency, cost, and model quality, review PRs (pull requests), and mentor engineers. Act as the principal builder of A1's AI systems by writing production code, designing ML pipelines, setting engineering standards, and directly shaping how the AI works in the real world.