Plaud

2 days ago

Machine Learning Engineer (Speech/Audio) - Singapore

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 21 hours agoStatus: Live

Job Summary

Own large-scale speech/audio data pipelines to collect, clean, label, augment, and quality-control data that powers model training. Contribute to fine-tuning and evaluating speech and language models to improve recognition for code-switching, names, and product terms. Drive domain adaptation using scenario-specific data, and build test sets and evaluation frameworks while benchmarking against open-source and commercial baselines.

Location: Singapore

Workplace: Onsite

Employment Type: Full time · Permanent

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Build and maintain large-scale speech/audio data pipelines for data collection, cleaning, filtering, labeling, augmentation, and quality control to support model training.
•Support model training and optimization via fine-tuning and evaluating speech/language models to improve recognition accuracy for code-switching, names, and product terms.
•Apply domain adaptation by fine-tuning models using scenario-specific data to improve recognition of industry-specific terms across key languages and verticals.
•Build test sets and evaluation frameworks (keyword/domain-lexicon based) and benchmark internal models against open-source and commercial baselines.
•Partner with senior speech engineers on term/hotword mining strategies based on ASR system output.

Pay and Benefits

Perks:EquityHealth Insurance

Key Requirements

•3+ years of hands-on experience in speech, machine learning, or large-scale data engineering.
•Experience with at least one: ASR/SpeechLLM model training, fine-tuning, or evaluation; LLM/general ML training or fine-tuning; or large-scale audio/video/text data pipeline work.
•Solid Python and PyTorch fundamentals.
•Experience with distributed data processing (e.g., Spark, Ray), supporting pipeline ownership at scale.

Experience:3+ yearsSpeechMachine learningData engineering

Tech Stack:PythonPyTorchSparkRayASRSpeechLLMLLMGeminiClaude Code

Company Brief

Plaud

Builds AI-native hardware and software note-taking devices and apps (Plaud NOTE, NotePin) that record, transcribe, summarize, and extract insights from conversations to boost professional productivity.

Industry: Hardware Devices

Company Size: Medium (51 to 250 employees)

Revenue: USD 100M to 250M

Growth: Scaleup

Funding: Bootstrapped

Headquarters: San Francisco, United States

Founded: 2021

Website LinkedIn

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Plaud

2 days ago

Machine Learning Engineer (Speech/Audio) - Singapore

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 21 hours agoStatus: Live

Job Summary

Location: Singapore

Workplace: Onsite

Employment Type: Full time · Permanent

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Build and maintain large-scale speech/audio data pipelines for data collection, cleaning, filtering, labeling, augmentation, and quality control to support model training.
•Support model training and optimization via fine-tuning and evaluating speech/language models to improve recognition accuracy for code-switching, names, and product terms.
•Apply domain adaptation by fine-tuning models using scenario-specific data to improve recognition of industry-specific terms across key languages and verticals.
•Build test sets and evaluation frameworks (keyword/domain-lexicon based) and benchmark internal models against open-source and commercial baselines.
•Partner with senior speech engineers on term/hotword mining strategies based on ASR system output.

Pay and Benefits

Perks:EquityHealth Insurance

Key Requirements

•3+ years of hands-on experience in speech, machine learning, or large-scale data engineering.
•Experience with at least one: ASR/SpeechLLM model training, fine-tuning, or evaluation; LLM/general ML training or fine-tuning; or large-scale audio/video/text data pipeline work.
•Solid Python and PyTorch fundamentals.
•Experience with distributed data processing (e.g., Spark, Ray), supporting pipeline ownership at scale.

Experience:3+ yearsSpeechMachine learningData engineering

Tech Stack:PythonPyTorchSparkRayASRSpeechLLMLLMGeminiClaude Code

Company Brief

Plaud

Industry: Hardware Devices

Company Size: Medium (51 to 250 employees)

Revenue: USD 100M to 250M

Growth: Scaleup

Funding: Bootstrapped

Headquarters: San Francisco, United States

Founded: 2021

Website LinkedIn