{"id":1482931,"url":"https://alion.io/job/asapp-speech-software-engineer","title":"Speech Software Engineer","company":{"id":628,"name":"ASAPP","domain":"asapp.com","url":"https://alion.io/company/asapp","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-14T06:34:16Z"}},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States","Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":215000,"max":235000,"currency":"USD","period":"year","gross":null,"usd_annual":235000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Go","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"Speech Recognition","optional":false},{"name":"Text-to-Speech","optional":false}],"status":"live","first_seen_at":"2026-01-12T22:09:43Z","employer_posted_date":"2026-01-12","last_verified_at":"2026-10-14T14:25:57Z","board_verified":true,"closed_at":null,"days_open":274,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":274},"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.\nWe’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.\nWe 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.\nYou 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.\nWhat you'll do\nSpeech Model Optimization & Applied Research:\nTune and optimize ASR and TTS models for real-world call center environments, improving transcription accuracy, noise robustness, and speaker variability\nImprove spoken output naturalness by refining prosody, pacing, number and spelling pronunciation, and conversational flow\nBalance latency vs. quality tradeoffs in streaming speech pipelines to maintain real-time responsiveness\nEvaluate and integrate emerging speech technologies (e.g., noise suppression, voice activity detection, diarization) to measurably improve performance\nVoice Infrastructure & Systems Engineering\nArchitect and modernize a scalable, high-availability voice infrastructure that replaces legacy systems\nBuild multi-threaded, low-latency server frameworks capable of handling thousands of concurrent real-time audio streams\nDesign and operate streaming ASR → LLM → TTS pipelines that power live AI-driven customer conversations\nDevelop robust media stream handling to ensure reliable audio flow between telephony providers, clients, and ML services\nEvaluation, Observability & Quality\nDefine and implement speech quality evaluation frameworks, including WER/CER analysis, latency tracking, and perceptual TTS metrics\nBuild tooling and dashboards to monitor production performance and detect regressions in accuracy, latency, or naturalness\nCreate load-testing and simulation tools to model high-concurrency, real-world voice traffic\nCross-Functional Collaboration\nPartner with Speech Scientists and ML Researchers to productionize new ASR and TTS models\nWork with Security and Compliance teams to ensure voice data handling meets enterprise and regulatory standards\nCollaborate with Product teams to translate conversational quality requirements into measurable system improvements\nWhat you'll need\nCore Engineering Background\n5+ years of software engineering experience building and operating production-grade distributed systems\nStrong proficiency in Golang or Python (or willingness to become an expert quickly)\nExperience designing low-latency, high-concurrency systems, ideally involving real-time media or streaming data\nSpeech & Audio Expertise\nPractical experience working with ASR and/or TTS systems in applied or production environments\nUnderstanding of how to adapt and tune speech models for domain-specific use cases\nFamiliarity with speech quality metrics such as WER, CER, MOS, latency, and streaming stability\nStrong grasp of audio fundamentals, including sample rates, codecs (Opus, G.711), buffering, packet loss, and jitter\nApplied ML for Speech\nExperience evaluating model performance and running structured experiments to improve transcription accuracy and speech naturalness\nComfort working with modern ML tooling and model APIs to fine-tune, adapt, or post-process speech model outputs\nAbility to make pragmatic tradeoffs between model quality, compute cost, and real-time constraints\nWhat we'd like to see\nExperience with noise reduction, echo cancellation, VAD, diarization, or other speech enhancement technologies\nFamiliarity with forced alignment techniques or phoneme/word-level timing models\nHands-on experience deploying ML services with Kubernetes, Docker, and cloud platforms (AWS/GCP/Azure)\nKnowledge of event-driven and asynchronous systems (e.g., async I/O, event loops, streaming frameworks)\nExperience analyzing large-scale speech or conversation datasets to drive model or system improvements","description_format":"text","description_chars":5093,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Continuous 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