Senior Software Engineer, Machine Learning (EST or EMEA)

<div class="content-intro"><h2><strong>About AssemblyAI</strong></h2> <p>AssemblyAI builds the best-in-class Speech AI models powering the next generation of voice applications. Our models serve 600M+ inference calls monthly, process 1M+ hours of audio daily, and power 2 billion+ end-user experiences—from voice agents and meeting assistants to contact centers and medical scribes. Companies like Zoom, Granola, Fireflies, Cluely, and Calabrio rely on AssemblyAI to ship production-ready voice AI.</p> <p>We're at an inflection point in Speech AI. We released Universal-Streaming in mid-2025, and it has quickly earned its place as the model offering the best accuracy-latency-cost tradeoff on the market. Our research team drives these advances and ships with relentless velocity. Since releasing Universal-Streaming, we've already launched <a href="https://www.assemblyai.com/blog/streaming-keyterms-prompting">keyterms prompting feature</a> and <a href="https://www.assemblyai.com/blog/introducing-multilingual-universal-streaming">multilingual support</a>—with more significant improvements on the roadmap.</p> <p>We've raised $115M+ from Accel, Insight Partners, Y Combinator's AI Fund, Patrick and John Collison, Nat Friedman, and Daniel Gross. We're a remote team building one of the next great AI companies—and we're looking for researchers who will shape its future.</p></div><h2><strong>About the role:</strong></h2> <p>We’re looking for a Senior Machine Learning Engineer to accelerate our AI research-to-production pipeline. You’ll build and improve the infrastructure that enables our research team to rapidly deploy and safely test new models, while helping ensure our production inference systems remain efficient, scalable, and reliable. You’ll identify gaps and opportunities in our ML infrastructure, scope solutions to ambiguous technical problems, and help set the technical direction for how we bridge research innovation and production reliability. This role requires a strong backend engineering background in distributed systems and containerization, and a track record of independently driving projects from concept to delivery. This is a cross-functional role that requires close collaboration with both research teams developing models and engineering teams supporting the broader platform.</p> <h2><strong>What You’ll Do:</strong></h2> <ul> <li>Design and implement tooling that enables researchers to quickly deploy and evaluate new models in production </li> <li>Design, build, and maintain high-performance, cost-efficient inference pipelines, making architectural decisions about scaling, reliability, and cost trade-offs</li> <li>Proactively identify and resolve infrastructure bottlenecks, proposing and scoping improvements to iteration speed and production reliability</li> <li>Develop and maintain user-facing APIs that interact with our ML systems</li> <li>Implement comprehensive observability solutions to monitor model performance and system health</li> <li>Troubleshoot and lead resolution of complex production issues across distributed systems, driving root-cause analysis and implementing preventive measures</li> <li>Set the direction for and continuously improve our MLOps practices, identifying the highest-impact opportunities to reduce friction between research and production.</li> <li>Collaborate closely with research and engineering teams to align on technical direction, and help onboard and mentor engineers on ML infrastructure best practices.</li> </ul> <h2><strong>What You’ll Need:</strong></h2> <ul> <li>Strong backend engineering experience with Python</li> <li>Experience building and operating distributed, containerized applications, preferably on AWS </li> <li>Proficiency implementing observability solutions (monitoring, logging, alerting, tracing) for production systems</li> <li>Ability to design and implement resilient, scalable architectures</li> <li>Track record of independently scoping and delivering complex technical proj ... (truncated, view full listing at source)