Director of AI Data Engineering (USA)
Director-level role leading the data engineering organization behind voice AI: building scalable data pipelines, multilingual speech/audio training datasets, annotation operations, and dataset governance for NLP, speech recognition, and text-to-speech models. Remote anywhere in the US; the employer is Blue Signal Search's AI client.
- Compensation
- Not disclosed
- Location
- Remote, Nationwide (United States)Our client, an...
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 10+ years of experience developing or leading data infrastructure supporting machine learning research, AI model development, or large-scale training environments
- Proven experience leading technical engineering organizations, including managers or senior ICs supporting AI research and data platform initiatives
- Direct experience with speech and audio datasets, including corpus design, annotation workflows, transcription quality, and quantitative dataset evaluation
- Strong understanding of NLP, speech-to-text, ASR, and TTS technologies as they relate to AI training data
- Experience designing and scaling data pipelines for audio processing, multilingual datasets, training data ingestion, and ML workflows
- Experience directing training data acquisition, multilingual speech collection, external annotation partners, and data quality improvement initiatives
- Production experience owning dataset versioning, reproducibility, lineage, or governance processes supporting ML experimentation
- Strong technical leadership, communication, and strategic planning skills, able to align engineering teams and research stakeholders
Full description
Director of AI Data EngineeringLocation: Remote, Nationwide (United States)
Our client, an innovative organization advancing the next generation of artificial intelligence and intelligent voice technologies, is seeking a Director of AI Data Engineering to lead the development of sophisticated data ecosystems powering speech recognition, natural language processing (NLP), and text-to-speech technologies. This is an exciting opportunity to shape the infrastructure behind next-generation voice AI, building the data pipelines, multilingual training datasets, and scalable engineering capabilities that enable AI models to understand and generate natural human speech. The ideal candidate will combine deep technical expertise in speech and audio data with strategic engineering leadership to accelerate AI research and innovation.
This Role Offers:
- Opportunity to lead high-impact engineering initiatives at the intersection of AI, speech recognition, and natural language technologies.
- Strategic ownership of data infrastructure supporting sophisticated voice AI and machine learning research.
- Meaningful influence over technical architecture, training data strategies, and engineering investments.
- Exposure to complex challenges involving multilingual speech processing, audio data quality, and large-scale AI model development.
Focus:
- Lead the architecture and evolution of scalable data engineering platforms supporting NLP, speech recognition, text-to-speech applications, and AI model training.
- Direct the development of sophisticated data pipelines for collecting, processing, transforming, and delivering large volumes of speech, audio, and language training data.
- Oversee training data acquisition strategies, including speech corpus development, multilingual dataset sourcing, annotation operations, and external data partnerships.
- Collaborate with AI researchers and speech technology specialists to optimize audio data preparation, speech synthesis training datasets, and model training effectiveness.
- Establish standards for multilingual speech datasets, including language coverage, transcription accuracy, annotation consistency, and data quality measurement.
- Lead and mentor engineering managers and senior technical contributors responsible for research data infrastructure, pipeline development, and data operations.
- Implement scalable frameworks for dataset versioning, lineage, reproducibility, and governance to support reliable model experimentation and production readiness.
- Partner with machine learning and NLP research teams to anticipate evolving training data requirements, improve data accessibility, and accelerate model development.