... annotation versioning. • Build tooling that supports the full audio data lifecycle -- from raw ingestion and quality filtering through augmentation, segmentation, and training split generation ...
... annotation versioning. • Build tooling that supports the full audio data lifecycle -- from raw ingestion and quality filtering through augmentation, segmentation, and training split generation ...
... annotation versioning. • Build tooling that supports the full audio data lifecycle -- from raw ingestion and quality filtering through augmentation, segmentation, and training split generation ...
... annotation versioning. • Build tooling that supports the full audio data lifecycle -- from raw ingestion and quality filtering through augmentation, segmentation, and training split generation ...
Strategic Projects Lead - Audio Data
Redwood City, CA · Hybrid
$140K - $160K/yr
Proven success delivering six-figure+ data collection or annotation contracts end-to-end. * Technical Domain: Direct experience managing audio, speech, voice, or multimodal data projects.
Quick apply
Strategic Projects Lead - Audio Data
Redwood City, CA · Hybrid
$140K - $160K/yr
Proven success delivering six-figure+ data collection or annotation contracts end-to-end. * Technical Domain: Direct experience managing audio, speech, voice, or multimodal data projects.
Spanish Audio Evaluator - Expert
San Francisco, CA · Remote
$50/hr
Spanish (Spain) Audio Generalist Evaluator Expert Type: Contract Compensation: $50/hour Location ... Experience with transcription, annotation, localization, evaluation, or research workflows in ...
Quick apply
Spanish Audio Evaluator - Expert
San Francisco, CA · Remote
$50/hr
Spanish (Spain) Audio Generalist Evaluator Expert Type: Contract Compensation: $50/hour Location ... Experience with transcription, annotation, localization, evaluation, or research workflows in ...
In this role, you will work on transcription, annotation, and evaluation tasks that help train and ... Audio & Video - Listen to, analyse, and transcribe audio and video content in Russian, following ...
In this role, you will work on transcription, annotation, and evaluation tasks that help train and ... Audio & Video - Listen to, analyse, and transcribe audio and video content in Russian, following ...
Applied Researcher, Audio
San Francisco, CA · On-site
$200K - $350K/yr
... and annotation * Set new standards for how we evaluate and benchmark our audio models What You ... Bring * Strong applied mindset and ability to balance scientific novelty with product impact.
Applied Researcher, Audio
San Francisco, CA · On-site
$200K - $350K/yr
... and annotation * Set new standards for how we evaluate and benchmark our audio models What You ... Bring * Strong applied mindset and ability to balance scientific novelty with product impact.
AI Tutor - Danish
Palo Alto, CA · On-site
Work with technical staff to improve annotation tools for efficient audio workflows. BASIC QUALIFICATIONS: * Native proficiency in Danish with exposure to diverse accents, dialects, or regional ...
Quick apply
AI Tutor - Danish
Palo Alto, CA · On-site
Work with technical staff to improve annotation tools for efficient audio workflows. BASIC QUALIFICATIONS: * Native proficiency in Danish with exposure to diverse accents, dialects, or regional ...
... and audio datasets, and developing the annotation frameworks that give those datasets meaning for ML pipelines. On the production side, you will coach actors and non-actors through structured ...
... and audio datasets, and developing the annotation frameworks that give those datasets meaning for ML pipelines. On the production side, you will coach actors and non-actors through structured ...
Creative Producer - Special Projects
$144K - $263K/yr
... and audio datasets, and developing the annotation frameworks that give those datasets meaning for ML pipelines. On the production side, you will coach actors and non-actors through structured ...
Creative Producer - Special Projects
$144K - $263K/yr
... and audio datasets, and developing the annotation frameworks that give those datasets meaning for ML pipelines. On the production side, you will coach actors and non-actors through structured ...
Technical Program Manager, Data
San Francisco, CA · On-site
$152K - $196K/yr
Responsibilities : • Lead audio data collection and annotation efforts at Sesame. • Collaborate with research and product teams to understand and formalize their requirements. • Identify and ...
Technical Program Manager, Data
San Francisco, CA · On-site
$152K - $196K/yr
Responsibilities : • Lead audio data collection and annotation efforts at Sesame. • Collaborate with research and product teams to understand and formalize their requirements. • Identify and ...
The role focuses on executing data labeling and annotation tasks for speech and voice AI systems, ensuring the accuracy and integrity of audio and language data. Responsibilities : • Execute high ...
The role focuses on executing data labeling and annotation tasks for speech and voice AI systems, ensuring the accuracy and integrity of audio and language data. Responsibilities : • Execute high ...
Project Perseus | Speech & Voice AI Analyst - Portuguese (Portugal) Speakers
Los Angeles, CA · On-site
This role focuses on building datasets for AI systems by executing high-volume data labeling and annotation tasks across audio and language data. Responsibilities : • Execute high-volume data ...
Project Perseus | Speech & Voice AI Analyst - Portuguese (Portugal) Speakers
Los Angeles, CA · On-site
This role focuses on building datasets for AI systems by executing high-volume data labeling and annotation tasks across audio and language data. Responsibilities : • Execute high-volume data ...
Technical Program Manager, Data
San Francisco, CA · On-site
$200K - $260K/yr
Lead audio data collection and annotation efforts at Sesame. * Collaborate with research and product teams to understand and formalize their requirements. * Identify and manage internal resources and ...
Technical Program Manager, Data
San Francisco, CA · On-site
$200K - $260K/yr
Lead audio data collection and annotation efforts at Sesame. * Collaborate with research and product teams to understand and formalize their requirements. * Identify and manage internal resources and ...
... in handling audio and language data. Responsibilities : • Execute high-volume data labeling and annotation tasks across speech and voice datasets • Follow detailed guidelines to ensure ...
... in handling audio and language data. Responsibilities : • Execute high-volume data labeling and annotation tasks across speech and voice datasets • Follow detailed guidelines to ensure ...
... audio, speech, and language data to ensure models are trained on accurate inputs. Responsibilities : • Execute high-volume data labeling and annotation tasks across speech and voice datasets • ...
... audio, speech, and language data to ensure models are trained on accurate inputs. Responsibilities : • Execute high-volume data labeling and annotation tasks across speech and voice datasets • ...
... annotation tasks across speech and voice datasets • Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale • Work with audio and language data, including ...
... annotation tasks across speech and voice datasets • Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale • Work with audio and language data, including ...
... annotation tasks across speech and voice datasets • Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale • Work with audio and language data, including ...
... annotation tasks across speech and voice datasets • Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale • Work with audio and language data, including ...
Execute Data labelling and annotation tasks across speech and voice datasets. * Work with audio and language data, including transcription, categorization, and tagging. YOU ARE A FIT IF YOU'RE... * A ...
Quick apply
Execute Data labelling and annotation tasks across speech and voice datasets. * Work with audio and language data, including transcription, categorization, and tagging. YOU ARE A FIT IF YOU'RE... * A ...
Execute Data labelling and annotation tasks across speech and voice datasets. * Work with audio and language data, including transcription, categorization, and tagging. YOU ARE A FIT IF YOU'RE... * A ...
Quick apply
Execute Data labelling and annotation tasks across speech and voice datasets. * Work with audio and language data, including transcription, categorization, and tagging. YOU ARE A FIT IF YOU'RE... * A ...
Execute Data labelling and annotation tasks across speech and voice datasets. * Work with audio and language data, including transcription, categorization, and tagging. YOU ARE A FIT IF YOU'RE... * A ...
Quick apply
Execute Data labelling and annotation tasks across speech and voice datasets. * Work with audio and language data, including transcription, categorization, and tagging. YOU ARE A FIT IF YOU'RE... * A ...
Audio Annotation information
What is audio annotation?
Audio annotation is the process of labeling or tagging audio data with relevant information, such as identifying sounds, speech, speakers, or background noises. This process helps train machine learning models to recognize and understand audio content. Audio annotation can involve tasks like transcribing speech, marking segments with specific sounds, or categorizing audio clips by genre or emotion. It is widely used in developing applications for speech recognition, virtual assistants, and audio analysis.
What are the key skills and qualifications needed to thrive as an audio annotator, and why are they important?
To thrive as an Audio Annotator, you need strong attention to detail, excellent listening skills, and familiarity with linguistic concepts, often supported by relevant coursework or experience in linguistics or audio processing. Proficiency in annotation tools such as ELAN, Audacity, or Praat, as well as experience with data labeling platforms, is typically required. Strong organizational skills, patience, and the ability to work independently make someone stand out in this role. These skills ensure accurate and consistent audio data labeling, which is essential for training reliable AI and speech recognition systems.
What are some common challenges faced by audio annotators, and how can they be managed effectively?
Audio annotators often encounter challenges such as distinguishing overlapping voices, dealing with low-quality recordings, and maintaining consistency in labeling. To manage these, it's important to use high-quality headphones, familiarize yourself with annotation guidelines, and communicate regularly with your team to resolve ambiguities. Many organizations also provide regular feedback sessions and quality checks to ensure accuracy and support continuous improvement.
What are popular job titles related to Audio Annotation jobs in California?
For Audio Annotation jobs in California, the most frequently searched job titles are:
What job categories do people searching Audio Annotation jobs in California look for?
The top searched job categories for Audio Annotation jobs in California are:
What cities in California are hiring for Audio Annotation jobs?
Cities in California with the most Audio Annotation job openings:
Full-time
Re-posted 12 days ago
Job description
Job Summary:
Sanas is pioneering the future of human communication with its innovative speech AI platform. The Staff Data Engineer will own the infrastructure for processing raw audio data, ensuring high-quality training-ready data for AI models, while collaborating with AI research scientists and ML engineers.
Responsibilities:
• Design and implement large-scale data pipelines that ingest, transform, validate, and serve high-quality audio and metadata for AI model training, evaluation, and product telemetry.
• Own the lakehouse architecture — table format choices (Iceberg vs. Delta Lake), partitioning strategies, metadata management, and schema evolution — with a bias toward reproducibility and auditability.
• Build and maintain batch and streaming pipelines using Spark, Flink, and orchestration tooling (Airflow or Dagster), with a clear-eyed view of when each is the right tool.
• Develop and maintain pipelines purpose-built for the unique challenges of audio data: large file volumes, time-series feature extraction, speaker and language metadata, and annotation versioning.
• Build tooling that supports the full audio data lifecycle — from raw ingestion and quality filtering through augmentation, segmentation, and training split generation — with reproducibility guarantees at every stage.
• Partner with ML engineers and research scientists to design data schemas, sampling strategies, and evaluation datasets that accurately reflect production conditions.
• Own data pipelines that feed human-in-the-loop annotation workflows — ensuring clean round-trips between raw data, labeling platforms, and training-ready outputs.
• Instrument pipelines with observability, data quality checks, lineage tracking, and alerting — so failures surface fast and root causes are traceable.
• Drive build vs. buy decisions for data quality, observability, and cataloging tooling with a clear framework grounded in Sanas's scale and roadmap.
• Own disaster recovery design for critical data assets — training datasets, evaluation benchmarks, and model checkpoints.
• Set the technical bar for the data engineering team — review designs and code, establish patterns, and document decisions in a way that raises the floor for everyone.
• Work cross-functionally with AI research, infrastructure, product, and legal to align data architecture with business needs and regulatory requirements.
• Contribute to hiring — identify strong candidates, conduct technical interviews, and help define what great looks like for data engineering at Sanas.
Qualifications:
Required:
• 5+ years of experience in data engineering, ML infrastructure, or data platform roles.
• Deep expertise building distributed batch and streaming data systems in production.
• Strong command of data processing frameworks: Spark, Flink, and Ray; and orchestrators: Airflow or Dagster.
• Hands-on experience with cloud data platforms — Snowflake, Databricks, or ClickHouse — and object storage (S3, GCS) on AWS or GCP.
• Solid understanding of data lifecycle management: privacy, security, compliance, and reproducibility from ingestion through model training.
• Proven ability to work directly with ML researchers and engineers to translate model requirements into data infrastructure decisions.
Preferred:
• Direct experience with audio data pipelines — file handling at scale, time-series features, speaker metadata, or audio annotation tooling.
• Familiarity with ASR, TTS, or speech enhancement model training workflows and the data requirements specific to each.
• Experience with MLOps tooling — experiment tracking, dataset versioning (DVC, LakeFS), and training pipeline orchestration.
Company:
Sanas is a real-time speech-understanding platform that modulates accents while preserving voices and emotions for natural interactions. Founded in 2020, the company is headquartered in Palo Alto, USA, with a team of 51-200 employees. The company is currently Growth Stage.
Sanas is pioneering the future of human communication with its innovative speech AI platform. The Staff Data Engineer will own the infrastructure for processing raw audio data, ensuring high-quality training-ready data for AI models, while collaborating with AI research scientists and ML engineers.
Responsibilities:
• Design and implement large-scale data pipelines that ingest, transform, validate, and serve high-quality audio and metadata for AI model training, evaluation, and product telemetry.
• Own the lakehouse architecture — table format choices (Iceberg vs. Delta Lake), partitioning strategies, metadata management, and schema evolution — with a bias toward reproducibility and auditability.
• Build and maintain batch and streaming pipelines using Spark, Flink, and orchestration tooling (Airflow or Dagster), with a clear-eyed view of when each is the right tool.
• Develop and maintain pipelines purpose-built for the unique challenges of audio data: large file volumes, time-series feature extraction, speaker and language metadata, and annotation versioning.
• Build tooling that supports the full audio data lifecycle — from raw ingestion and quality filtering through augmentation, segmentation, and training split generation — with reproducibility guarantees at every stage.
• Partner with ML engineers and research scientists to design data schemas, sampling strategies, and evaluation datasets that accurately reflect production conditions.
• Own data pipelines that feed human-in-the-loop annotation workflows — ensuring clean round-trips between raw data, labeling platforms, and training-ready outputs.
• Instrument pipelines with observability, data quality checks, lineage tracking, and alerting — so failures surface fast and root causes are traceable.
• Drive build vs. buy decisions for data quality, observability, and cataloging tooling with a clear framework grounded in Sanas's scale and roadmap.
• Own disaster recovery design for critical data assets — training datasets, evaluation benchmarks, and model checkpoints.
• Set the technical bar for the data engineering team — review designs and code, establish patterns, and document decisions in a way that raises the floor for everyone.
• Work cross-functionally with AI research, infrastructure, product, and legal to align data architecture with business needs and regulatory requirements.
• Contribute to hiring — identify strong candidates, conduct technical interviews, and help define what great looks like for data engineering at Sanas.
Qualifications:
Required:
• 5+ years of experience in data engineering, ML infrastructure, or data platform roles.
• Deep expertise building distributed batch and streaming data systems in production.
• Strong command of data processing frameworks: Spark, Flink, and Ray; and orchestrators: Airflow or Dagster.
• Hands-on experience with cloud data platforms — Snowflake, Databricks, or ClickHouse — and object storage (S3, GCS) on AWS or GCP.
• Solid understanding of data lifecycle management: privacy, security, compliance, and reproducibility from ingestion through model training.
• Proven ability to work directly with ML researchers and engineers to translate model requirements into data infrastructure decisions.
Preferred:
• Direct experience with audio data pipelines — file handling at scale, time-series features, speaker metadata, or audio annotation tooling.
• Familiarity with ASR, TTS, or speech enhancement model training workflows and the data requirements specific to each.
• Experience with MLOps tooling — experiment tracking, dataset versioning (DVC, LakeFS), and training pipeline orchestration.
Company:
Sanas is a real-time speech-understanding platform that modulates accents while preserving voices and emotions for natural interactions. Founded in 2020, the company is headquartered in Palo Alto, USA, with a team of 51-200 employees. The company is currently Growth Stage.