Senior Applied Scientist, Speech
Leads the development and production deployment of large-scale ASR and TTS systems for conversational intelligence products. The role requires 5+ years of industry experience, deep speech-model expertise, and strong software engineering and ML operations capabilities.
About the job
Responsibilities
- Architect, build, and evolve large-scale automatic speech recognition (ASR) and text-to-speech (TTS) systems for speech understanding across millions of conversations.
- Design and implement training, fine-tuning, post-training, and inference strategies for speech models using PyTorch, balancing quality, latency, cost, and reliability.
- Improve model architectures, loss functions, decoding strategies, and training techniques for speech models.
- Own end-to-end machine learning system lifecycles, from research prototyping through production deployment, monitoring, iteration, and maintenance.
- Partner with product and infrastructure teams to translate research into scalable, production-grade systems.
- Improve model performance, robustness, observability, and operational excellence using real-world conversational data at scale.
- Set technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment.
- Resolve complex problems involving model behavior, data quality, scaling, and system interactions.
- Mentor engineers, influence team standards, review designs, and contribute to strong technical decision-making.
Requirements
- Bachelor's or master's degree in Computer Science or a related field; PhD preferred.
- 5+ years of relevant industry experience.
- Deep hands-on experience building and fine-tuning speech or foundation models, with production experience in ASR and/or TTS systems.
- Strong knowledge of modern ML research and the ability to evaluate papers and identify production-worthy innovations.
- Experience deploying, scaling, monitoring, and operating ML systems in production across training, inference, and serving infrastructure.
- Experience with large-scale speech and conversational datasets, including preprocessing, augmentation, quality analysis, and labeling strategies.
- Ability to lead technical projects independently and make sound architectural decisions in ambiguous problem spaces.
- Experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration.
Compensation
- Base salary range: $230,000–$265,000 USD per year.
Skills
Asr, Tts, PyTorch, Machine Learning, Foundation Models, Speech Models, Model Fine-Tuning, ML Infrastructure, Data Pipelines, Model Deployment, Inference, Cloud Computing, Conversational Data, Agentic Systems, Multi-Model Orchestration
Similar jobs
ML Engineering jobs
Baselayer
San Francisco, CA
Senior AI Engineer responsible for production LLM agents that enrich business identity data through web discovery, verification, classification, and risk scoring. The role requires strong asynchronous Python, agent and evaluation expertise, browser automation, and experience operating AI systems in production.
Ambience Healthcare
San Francisco, CA
Build and improve production AI systems for clinical products, owning evaluations, model behavior, agentic workflows, data flywheels, deployment, and observability. The role requires 5+ years of production ML or applied AI experience, strong Python and modern ML framework skills, and hands-on debugging expertise.
Dialpad
United States
Leads development of speech models, decoders, and low-latency inference systems for next-generation voice agents. Requires 5+ years in speech ML or related audio AI, strong Python and PyTorch experience, and the ability to guide technical direction and mentor engineers.
Ontario, Canada
Design, build, and deploy production ML systems for recommendations, search, ranking, and advertising at internet scale. Own the full ML lifecycle from modeling to monitoring with strong cross-functional collaboration.
Fetch
United States
Build and operate low-latency machine learning systems for ad ranking, relevance, and optimization, including feature pipelines, experimentation, evaluation, and production inference. The role requires 6+ years of software engineering experience, strong Python skills, AWS experience, and practical LLM application experience.