Senior Software Engineer, Machine Learning

• 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