Machine Learning Engineer | Audio, Speech, Acoustics

Machine Learning Engineer | Audio, Speech, Acoustics & Sound

Reolink , a leader in intelligent visual technology for homes and businesses, was founded in 2009 by a group of engineers with a strong commitment to and passion for smarter security solutions.
Our products are now trusted by millions of users across more than 110 countries and regions worldwide. Building on this trust, we continue expanding our presence and bringing our innovations to more markets around the globe. Reolink remains committed to delivering advanced, reliable, and user‑centric solutions that empower people to protect what matters most.

• Audio Algorithm Engineer (Master / PhD Holder)
• Work Days Per Week
• Medical Benefits Provided
• Entitled to Yearly Bonus & Performance Bonus

Job Requirements

• Master's Degree or PhD Holder in Computer Science, Electrical Engineering, Signal Processing, Acoustics,
  Artificial Intelligence or a related field, with a focus on speech or audio.
• Hands‑on experience training and evaluating deep learning models for audio, in signal‑level tasks (such as speech enhancement or voice activity detection), understanding tasks (such as speech recognition, sound event detection or audio captioning), or both; able to pick up new audio tasks quickly.
• Solid foundation in audio signal processing: STFT, filterbank and mel features, pitch and harmonics, room
  acoustics and reverberation, SNR and level.
• Familiar with audio front‑end processing (noise suppression, echo cancellation, dereverberation, gain control andvoice activity detection), and with how each step affects listeners and downstream models.
• Able to design and train small, streaming models under compute, memory and latency budgets; familiar withquantisation, pruning and knowledge distillation, and with measuring model size, MACs and real‑time factor.
• Familiar with audio understanding models: speech recognition (CTC, transducer, and attention or LLM‑baseddecoders), sound event detection and audio classification, audio captioning, and pretrained audio encoders suchas Whisper, BEATs or CLAP.
• Familiar with large audio models: audio‑language models, fine‑tuning them (including parameter‑efficient methodssuch as LoRA), and connecting audio models to LLMs for question answering, summarisation or dialogue.
• Experience in speech is highly valued, especially multilingual speech recognition (including WER and CER scoring with text normalisation, for languages with and without spaces between words) and voice interaction (end‑of‑turn detection and turn‑taking).
• Experience with audio data and evaluation: augmentation and synthetic mixing (noise, room impulse responses,speed perturbation, SpecAugment), test sets split by speaker, device and room rather than only by clip, and theright metric for each task.
  Strong programming skill in Python and PyTorch; familiar with Linux, multi‑GPU training, experiment tracking andgit.
• Experience with far‑field audio (for example the CHiME, DNS or AEC challenges) or on‑device deployment
  (quantisation‑aware training, ONNX / TFLite, sherpa‑onnx or vendor NPU toolchains) is a plus.
• Experience with voice agents (ASR, LLM and TTS cascades, or full‑duplex speech models such as Moshi) or audiovisual models is a plus

Job Responsibilities

• Speech Recognition: Develop streaming, multilingual speech recognition and keyword spotting for far‑field audiofrom a single camera microphone. Adapt, fine‑tune and compress open models (for example Whisper, Parakeet,Qwen3‑ASR) and compare them on our audio.
• Voice Interaction: Develop voice activity detection, end‑of‑turn detection, turn‑taking and barge‑in (the user
  interrupts while the device speaks). Compare cascades (ASR, LLM, TTS) with full‑duplex speech models.
• Speech Enhancement: Develop noise suppression, echo cancellation, dereverberation and target‑speaker
  extraction for one microphone. Measure what each step does for the people who listen and for the models thatcome after it.
• Sound Events and Audio Understanding: Develop sound event detection, audio classification and audio captioning.
• Model Training and Evaluation: Run audio experiments end to end, from data preparation to training, fine‑tuningand ablations. Build evaluation harnesses: WER and CER for each language, miss and false‑alarm rates fordetection, event‑detection F1 or mAP, end‑of‑turn latency, PESQ, DNSMOS and echo‑cancellation metrics.
• Slice the results by distance, level, SNR and room, and test on real camera recordings.
• Data Analysis and Preprocessing: Build audio data pipelines: speech and sound corpora (with commercial licenceschecked), synthetic mixing with room impulse responses and recorded c