Speech Software Engineer

platform. This is not just a systems role - you will operate at the intersection of speech research, model optimization, and production engineering, ensuring our ASR and TTS systems meet the demanding quality, latency, and reliability requirements of enterprise call centers. You will help evolve our speech stack to deliver human-like, low-latency voice interactions at massive scale, tuning and adapting modern speech models to perform in noisy, real-world customer environments. You will work closely with Speech Scientists, ML Researchers, and Infrastructure Engineers to bridge cutting-edge speech technology with hardened production systems. What you’ll do Speech Model Optimization & Applied Research: Tune and optimize ASR and TTS models for real-world call center environments, improving transcription accuracy, noise robustness, and speaker variability Improve spoken output naturalness by refining prosody, pacing, number and spelling pronunciation, and conversational flow Balance latency

vs. quality tradeoffs in streaming speech pipelines to maintain real-time responsiveness Evaluate and integrate emerging speech technologies (e.g., noise suppression, voice activity detection, diarization) to measurably improve performance Voice Infrastructure & Systems Engineering Architect and modernize a scalable, high-availability voice infrastructure that replaces legacy systems Build multi-threaded, low-latency server frameworks capable of handling thousands of concurrent real-time audio streams Design and operate streaming ASR * LLM * TTS pipelines that power live AI-driven customer conversations Develop robust media stream handling to ensure reliable audio flow between telephony providers, clients, and ML services Evaluation, Observability & Quality Define and implement speech quality evaluation frameworks, including WER/CER analysis, latency tracking, and perceptual TTS metrics Build tooling and dashboards to monitor production performance and detect regressions in accuracy, latency, or naturalness Create load-testing and simulation tools to model high-concurrency, real-world voice traffic Cross-Functional Collaboration Partner with Speech Scientists and ML Researchers to productionize new ASR and TTS models Work with Security and Compliance teams to ensure voice data handling meets enterprise and regulatory standards Collaborate with Product teams to translate conversational quality requirements into measurable system improvements What you’ll need Core Engineering Background 5+ years of software engineering experience building and operating production-grade distributed systems Strong proficiency in Golang or Python (or willingness to become an expert quickly) Experience designing low-latency, high-concurrency systems, ideally involving real-time media or streaming data Speech & Audio Expertise Practical experience working with ASR and/or TTS systems in applied or production environments Understanding of how to adapt and tune speech models for domain-specific use cases Familiarity with speech quality metrics such as WER, CER, MOS, latency, and streaming stability Strong grasp of audio fundamentals, including sample rates, codecs (Opus, G.711), buffering, packet loss, and jitter Applied ML for Speech Experience evaluating model performance and running structured experiments to improve transcription accuracy and speech naturalness Comfort working with modern ML tooling and model APIs to fine-tune, adapt, or post-process speech model outputs Ability to make pragmatic tradeoffs between model quality, compute cost, and real-time constraints What we’d like to see

• Experience with noise reduction, echo cancellation, VAD, diarization, or other speech enhancement technologies
• Familiarity with forced alignment techniques or phoneme/word-level timing models
• Hands-on experience deploying ML services with Kubernetes, Docker, and cloud platforms (AWS/GCP/Azure)
• Knowledge of event-driven and asynchronous systems (e.g., async I/O, event loops, streaming frameworks)
• Experience analyzing large-scale speech or conversation datasets to drive model or system improvements