AI Systems Engineer Jobs

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Lead the design and implementation of a scalable, high-availability voice infrastructure that replaces legacy systems. Build and refine multi-threaded server frameworks capable of handling thousands of concurrent, real-time audio streams with minimal jitter and latency. Deploy robust ASR > LLM > TTS pipelines that process thousands of calls concurrently. Develop robust logic for handling media streams, ensuring seamless audio data flow between clients and machine learning models. Build advanced monitoring and load-testing tools specifically designed to simulate high-concurrency voice traffic. Partner with Speech Scientists and Research Engineers to integrate state-of-the-art models into a production-ready environment.

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Drive the architectural vision for the GenerativeAgent product by designing and building a highly scalable, multi-agent platform for real-time voice and text customer service experiences across various industries. Act as a technical authority and advisor for multiple engineering teams, develop system design and technical roadmaps, and define communication, state management, and orchestration patterns for multi-agent systems. Design and implement scalable, multi-tenant deployment architectures, own and define system-level SLOs/SLIs focusing on latency, cost-efficiency, and fault tolerance, identify systemic risks with proactive mitigation strategies, partner with Security and Compliance teams to meet regulatory and security requirements, lead post-incident analysis and improvements, and collaborate cross-functionally with Product, Customer Engineering, Site Reliability Engineering, TPMs, and Research to translate business requirements into system designs and productionize ML research. Mentor senior engineers and communicate complex technical concepts to both technical and non-technical stakeholders.

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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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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 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 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 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 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 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 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 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 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 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 gain 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 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 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.