Machine Learning Engineer (Speech/Audio) - Singapore

Plaud is building the real-world AI interface for professionals to amplify intelligence, elevate productivity and performance, loved by over 2,500,000 users worldwide since 2023. With a mission to amplify human intelligence, Plaud captures, structures, and compounds the intelligence generated in conversations — so humans can think better, decide faster, and execute with clarity.

Plaud Inc. is a Delaware-incorporated, San Francisco-based company pushing the boundary of human–AI intelligence through a hardware–software combination. With full ISO 27001, ISO 27701, SOC 2, GDPR, EN18031, and HIPAA compliances, Plaud is committed to the highest standards of data security and privacy protection. To learn more about Plaud, please visit https://www.plaud.ai and follow along on Instagram, X, Facebook, LinkedIn, and YouTube.

Plaud is building the next generation intelligence infrastructure and interfaces to capture, extract, and utilize intelligence from what people say, hear, see, and think.

• Plaud is a bootstrapped, skyrocketing, profitable company with a $300M revenue run rate achieved in just three years.

• Define the next-gen paradigm for human-AI interaction.

• Gain exposure to cutting-edge AI for Pro tools and play a direct role in our global expansion.

• Work with passionate teammates who value innovation, collaboration, and customer success.

• Grow your career in a culture that champions continuous learning and fast career development.

• Market-competitive compensation, global exposure, and a vibrant, creativity-fueled work atmosphere.

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  Own large-scale speech/audio data pipelines: build and maintain systems for data collection, cleaning, filtering, labeling, augmentation and quality control to support model training. Partner with senior speech engineers on term/hotword mining strategies based on ASR system output.

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  Support model training and optimization: contribute to fine-tuning and evaluating speech/language models (SpeechLLM or general LLM background both applicable), with focus on improving recognition accuracy for code-switching, names, and product terms — working alongside domain specialists on the technical algorithm design.

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  Domain adaptation: help fine-tune models using scenario-specific data to improve recognition of industry-specific terms across key languages and verticals.

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  Evaluation: build test sets and evaluation frameworks (keyword/domain-lexicon based); benchmark internal models against open-source and commercial baselines.

• Minimum 1 year of hands-on experience in speech, machine learning, or large-scale data engineering.

• Experience in AT LEAST ONE of the following:
  a) ASR/SpeechLLM model training, fine-tuning, or evaluation;
  b) LLM or general ML model training/fine-tuning;
  c) Large-scale audio, video, or text data pipeline work (e.g., tens of thousands of hours of audio, or TB-scale multimodal data).

• Solid Python and PyTorch fundamentals.

• Experience with distributed data processing (e.g., Spark, Ray) — moved up from nice-to-have, since responsibility #1 requires pipeline ownership at scale.

• Familiarity with SpeechLLM / speech SSL concepts, or exposure to models such as StepAudio or Qwen3-Omni.

• Prior experience building or contributing to hotword/contextual-biasing or code-switching ASR improvements.

• Publications at Interspeech, ICASSP, or other top AI venues, or speech-related patents.

• Experience owning a data workstream (tens/hundreds of thousands of hours of speech) in a speech-model training project.