About the role

We’re looking for a Senior Go Software Engineer to build real-time voice AI systems that set the standard.

Your responsibilities will include:

Design and enhance backend infrastructure focusing on scalability, performance, and reliability. Implement features and maintain the systems that power our next-generation products.
Expertly manage concurrency and parallelism in Go using goroutines, channels, and synchronization primitives like mutexes and wait groups.
Develop robust networking and streaming capabilities, including experience with network programming, sockets, and protocols like WebSockets and WebRTC.
Lead performance optimization initiatives by profiling Go code, reducing latency, and efficiently managing memory and garbage collection.
Build fault-tolerant systems with strong recovery mechanisms and failover strategies to maintain service continuity.
Implement comprehensive logging and tracing using tools such as zap, klog, OpenTelemetry, and Jaeger to enhance monitoring and troubleshooting.
Apply Test-Driven Development (TDD) and engage in Pair Programming to ensure high code quality and promote team collaboration.
Participate actively in code reviews, offering constructive feedback to uphold code quality and adhere to best practices.
Stay informed on the latest in software development and contribute to the team's continuous learning and improvement.

At least 5 years of professional experience with Go, along with a track record of developing scalable and extensible systems.
Additional proficiency in Python and familiarity with infrastructure containerization, DevOps & MLOps tools (preferably GCP or AWS).
Extensive experience in creating and maintaining robust APIs and implementing advanced CI/CD pipelines.
A strong foundation in software engineering principles and a commitment to leading-edge practices including TDD, pair programming, and continuous integration.

Exposure to voice technologies like WebRTC and Twilio.
A background in AI, particularly around Large Language Models, Speech to Text, and Voice Synthesis applications.
Experience in AI-related fields, such as low-latency audio stream processing or leveraging LLMs in product environments.
Knowledge of distributed computing and cloud infrastructure at scale.