Research Scientist, Speech & Audio

InnoData

. Define how Innodata designs, structures, and evaluates audio data for speech and audio models .

Posted 9/24/2026full-timeRemote • United StatesMid-LevelSenior💰 $160,000 - $185,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and evaluating audio data for speech models, with a strong foundation in machine learning methodologies and hands-on experience in building and fine-tuning various speech and audio models. Proficient in collaborating with cross-functional teams to operationalize specifications and publish research findings.

Highest-signal resume keywords

Speech Or Audio ML ExperiencePyTorch FundamentalsFluency In ESPnet, NeMo, SpeechBrain, KaldiFirst-Author Publications In Interspeech, ICASSP, ASRUExperience With Multilingual And Accented Speech

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Hard Skills

Speech RecognitionText-To-SpeechSpeech-To-Speech ModelsDiarizationEvaluation MethodologiesData SpecificationsDataset Coverage ReasoningNoise And Room-Response SimulationTranscription SpecificationsAdversarial Evaluations

Soft Skills

Clear CommunicationCollaborationRigorous DocumentationCustomer EngagementProblem-Solving

Tools & Technologies

HuggingFaceForced AlignmentWER/CER MetricsSynthetic Audio PipelinesAugmented Audio Pipelines

Industry Keywords

ASRConversational VoiceSpeech NaturalnessStreaming LatencyCode-SwitchingDiarization Error RateSemantic AccuracyEmotional RangeParalinguistic RangeLow-Resource Speech

Tech Stack

Tools & technologies

PyTorchSwitching

About the role

Key responsibilities & impact

Define how Innodata designs, structures, and evaluates audio data for speech and audio models
Translate speech and audio model requirements into data specifications covering modalities, transcription and annotation schemas, sampling, and evaluation criteria
Build evaluation methodologies covering semantic accuracy, noise and accent robustness, code-switching, diarization error rate, speech naturalness and intelligibility, and streaming latency
Structure, enrich, and sample audio across languages, accents, acoustic conditions, demographics, emotional and paralinguistic range, scripted and spontaneous speech, and speaker configurations
Partner with transcription and linguistics leads to develop transcription specifications and quantify their impact on model results
Partner with audio solutions and engineering teams to ensure collected audio meets model objectives
Run fine-tuning and evaluation experiments with ablations linking data choices to measurable improvements
Design adversarial and stumping evaluations to identify speech-system failures
Publish benchmarks, methodology, and research papers
Work with annotation teams, subject-matter experts, and synthetic- and augmented-audio pipelines to operationalize specifications
Partner directly with customers and frontier labs building ASR, text-to-speech, speech-to-speech, conversational voice, diarization, and audio-language models

Requirements

What you’ll need

Roughly 5+ years of hands-on industry experience in speech or audio ML
A Bachelor's degree in computer science, electrical engineering, or a related technical or quantitative field is required
Trained and evaluated speech or audio models yourself — ASR, TTS, speech-to-speech, speaker, or audio-language models — with strong PyTorch fundamentals
Fluency in ESPnet, NeMo, SpeechBrain, Kaldi, HuggingFace, forced alignment, WER/CER, and related speech metrics
Hands-on experience with multilingual, accented, dialectal, low-resource, or code-switched speech
Experience with synthetic or augmented audio, including TTS pipelines, noise and room-response simulation
Built evaluation sets and reasoned about dataset coverage across conditions
First-author publications or strong open-source contributions at venues such as Interspeech, ICASSP, ASRU, SLT, or NeurIPS
Ability to work directly with customer and frontier-lab research scientists
Ability to explain data and modeling decisions clearly to expert and non-expert audiences
Rigorous, reproducible approach to experiments and documentation
Interest or hands-on experience in responsible-AI evaluation and red-teaming is a bonus