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Senior Software Engineer, Machine Learning at AssemblyAI. Location Information: United States. AssemblyAI is an applied artificial intelligence company. We use the latest deep learning technology to build practical products that bring futuristic ideas to life.. Our team includes researchers, engineers, and designers that have worked at some of the largest technology companies all over the world. Our main office is located in downtown San Francisco.. At AssemblyAI, we believe that cutting edge artificial intelligence technology should not be limited to only those with the funding or resources to invest in it.. Our goal is to help make creative, new ideas possible by making AI technology accessible to everyone through easy to use products, whether you are an independent developer, startup, or global company.. Design and implement tooling that enables researchers to quickly deploy and evaluate new models in production. Design, build, and maintain high-performance, cost-efficient inference pipelines, making architectural decisions about scaling, reliability, and cost trade-offs. Proactively identify and resolve infrastructure bottlenecks, proposing and scoping improvements to iteration speed and production reliability. Develop and maintain user-facing APIs that interact with our ML systems. Implement comprehensive observability solutions to monitor model performance and system health. Troubleshoot and lead resolution of complex production issues across distributed systems, driving root-cause analysis and implementing preventive measures. Set the direction for and continuously improve MLOps practices, identifying the highest-impact opportunities to reduce friction between research and production. Collaborate closely with research and engineering teams to align on technical direction, and help onboard and mentor engineers on ML infrastructure best practices.. Strong backend engineering experience with Python. Experience building and operating distributed, containerized applications, preferably on AWS. Proficiency implementing observability solutions (monitoring, logging, alerting, tracing) for production systems. Ability to design and implement resilient, scalable architectures. Track record of independently scoping and delivering complex technical projects from problem identification through production deployment. Comfort navigating ambiguity and making pragmatic technical decisions when requirements are unclear or evolving. MLOps experience, including familiarity with PyTorch and Kubernetes. Experience working in fast-paced environments where you owned technical direction for an area and drove projects with minimal oversight.. Experience collaborating with remote, globally distributed teams. Comfort working across the entire ML lifecycle from model serving to API development. Experience in audio-related domains (ASR, TTS, or other domains involving audio processing). Experience with other cloud providers. Familiarity with Bazel and monorepos. Experience with alternative ML inference frameworks beyond PyTorch. Experience with other programming languages. Experience mentoring junior engineers or onboarding teammates onto complex systems. Pay range:. $195K - $225K