🔥 23 hours ago
As a Developer Advocate, you will bridge engineering and developer relations by creating impactful content, engaging with the developer community, and driving adoption of Fireworks' AI platform.
Speech Software Engineer
📅 Jan 12
GolangPythonDistributed SystemsASR
📜 Description
- Tune ASR and TTS models for transcription accuracy, noise robustness, and speaker variability.
- Improve speech naturalness and balance quality with latency in streaming pipelines.
- Evaluate and integrate speech technologies such as noise suppression, voice activity detection, and diarization.
- Architect scalable voice infrastructure and build low-latency systems for thousands of concurrent audio streams.
- Design and operate streaming ASR-to-LLM-to-TTS pipelines and reliable media handling.
- Develop speech quality metrics, monitoring dashboards, load tests, and simulation tools; collaborate across research, infrastructure, security, and product teams.
🛠️ Requirements
- 5+ years of software engineering experience building and operating production-grade distributed systems.
- Proficiency in Golang or Python, or willingness to become an expert quickly.
- Experience designing low-latency, high-concurrency systems, ideally for real-time media or streaming data.
- Practical experience with ASR and/or TTS, speech model adaptation, and speech quality metrics such as WER, CER, and MOS.
- Knowledge of audio fundamentals, including sample rates, codecs, buffering, packet loss, and jitter.
- Experience evaluating models and running structured experiments; familiarity with ML tooling and model APIs.
- Preferred: speech enhancement, forced alignment, Kubernetes, Docker, cloud platforms, asynchronous systems, or large-scale speech datasets.
Full job description
At ASAPP, our mission is simple: deliver the best AI-powered customer experience—faster than anyone else. We are guided by principles that shape how we think, build, and execute, including deep customer obsession, purposeful speed, ownership, and a relentless focus on outcomes. We work in small, highly skilled teams, prioritize clarity over complexity, and continuously evolve through curiosity, data, and craftsmanship.
We’re building a globally diverse team of technologists and problem solvers who thrive in fast-paced environments, value collaboration, and approach every challenge with a Day 1 mindset. With hubs in New York City, Mountain View, Latin America, and India. If you’re driven by continuous learning, rapid iteration, and the challenge of building in a high-growth startup, this is more than a role—it’s a journey.
We are seeking a Senior Speech Software Engineer to drive both the infrastructure and applied speech intelligence behind our real-time voice AI 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.
ASAPP is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, age, or veteran status. If you have a disability and need assistance with our employment application process, please email us at careers@asapp.com to obtain assistance. #LI-AG1 #LI-Hybrid
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
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