Senior Machine Learning Software Engineer, AssemblyAI

• 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
• 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

Desirable

• 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 is a cross-functional role that requires close collaboration with both research teams developing models and engineering teams supporting the broader platform
• 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 our 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