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Lead AI Platform Engineer 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 scalable, future-proof data platforms optimized for AI research workloads. Build efficient data pipelines leveraging GCP's advanced services. Implement cost-effective storage and monitoring solutions for ML at scale. Create flexible training resource management with intelligent queuing. Optimize resource allocation for maximum training efficiency. Participate in on-call rotation to ensure system reliability. Lead adoption of cutting-edge ML tools and frameworks. Streamline existing workflows while introducing new tooling that reduces complexity. Enhance tooling and documentation to accelerate team velocity. Implement guardrails for cost, quality, and performance. Identify and eliminate technical bottlenecks in the training pipeline. 8+ years of experience in AI/ML Infrastructure, Research Platform Engineering, or related software engineering roles. 3+ years of professional experience working as an AI data and infrastructure setting or similar position. Strong proficiency in Python and SQL. Deep expertise with GCP services like BigTable, BigQuery, Dataproc, Dataflow. Experience with distributed processing frameworks (e.g., Apache Beam, PySpark). Familiarity with workflow orchestration tools (e.g., Airflow, Composer, Astronomer). Understanding of distributed training systems and data loading optimization. Experience with experiment tracking and training tooling. Ability to thrive in a startup environment with aggressive prioritization. Pay range:. $230K - $275K