AI Engineer - Audio/Speech

Key Responsibilities:

PHD Degree is Must have for this role.

• Design, develop, and deploy Large Audio Language Models (LALMs) capable of native audio understanding, reasoning, and generation.
• Build Large Audio Reasoning Models that perform complex chain-of-thought reasoning over speech and audio inputs, including medical, technical, and conversational domains.
• Contribute to Speech-to-Speech (S2S) system development, including speech understanding, dialogue management, and speech synthesis components.
• Research and implement alignment mechanisms between speech encoders and LLM backbones using lightweight adapters, LoRA, and efficient fine-tuning strategies.
• Design efficient speech tokenization and temporal compression techniques suitable for long-form audio reasoning and multi-turn spoken dialogue.
• Build comprehensive evaluation frameworks for audio reasoning capabilities, including benchmarks for speech QA, audio understanding, and reasoning accuracy.
• Optimize inference pipelines for low-latency, streaming applications in speech systems.
• Collaborate with cross-functional teams to transfer research innovations into production systems and customer-facing applications.
• Contribute to technical documentation, research write-ups, and publications at top-tier venues (NeurIPS, ICML, ACL, Interspeech).

Minimum Qualifications

• Master's degree (required) or Ph.D. (preferred) in Computer Science, Electrical Engineering, or a related field with a focus on speech, audio ML, or multimodal learning.
• 2+ years of industry or applied research experience in speech/audio AI, Large Language Models, or multimodal systems.
• Demonstrated applied research contributions through publications, patents, or shipped products in speech/audio AI or LLMs.
• Strong proficiency in Python and PyTorch, with hands-on experience in GPU-accelerated training for large-scale models.
• Solid understanding of speech and audio signal processing, acoustic modeling, and audio representations.
• Working knowledge of modern LLM architectures (Transformers, SSMs) and training paradigms including instruction tuning and alignment methods.
• Familiarity with modality alignment techniques: adapter-based integration, cross-modal attention, or audio-text fusion methods.
• Strong experimentation habits: clean code, systematic ablations, reproducibility, and clear technical communication.

Preferred Qualifications

• Publication record at top-tier venues (NeurIPS, ICML, ICLR, ACL, Interspeech, ICASSP) in audio language models, speech reasoning, or multimodal learning.
• Hands-on experience building or fine-tuning Large Audio Language Models (e.g., Qwen-Audio, SALMONN, LTU, Gemini Audio).
• Experience with speech representation pretraining (HuBERT, Wav2Vec 2.0, Whisper, WavLM) and discrete speech tokenization.
• Familiarity with Speech-to-Speech components: neural audio codecs (EnCodec, SoundStream), vocoders, or speech synthesis systems.
• Experience with audio reasoning benchmarks (AIR-Bench, MMAU, AudioBench) or building evaluation harnesses for audio QA.
• Hands-on experience with distributed training (FSDP, DeepSpeed) and inference optimization (ONNX, TensorRT, quantization).
• Familiarity with speech frameworks such as ESPnet, SpeechBrain, NVIDIA NeMo, or Fairseq.
• Experience with multilingual speech systems, code-switching, or domain adaptation for specialized applications (medical, legal, technical).
• Background in evaluating safety, bias, hallucination, or adversarial robustness in audio language models.

Technical Environment

• Core: PyTorch, CUDA, torchaudio/librosa, Hugging Face Transformers
• LLM Stack: Large language model backbones, lightweight adapters (LoRA, Q-Former), instruction tuning pipelines
• Audio Models: Neural audio codecs, speech encoders, vocoders, discrete speech tokenizers
• Infrastructure: Modern GPU clusters, experiment tracking (Weights & Biases), distributed training frameworks
• Deployment: FastAPI/gRPC for services, ONNX/TensorRT for optimized inference

What We Offer

• Competitive compensation package with comprehensive benefits
• Opportunity to work on cutting-edge Large Audio Language Models and audio reasoning research with real-world impact
• Collaboration with experienced applied scientists and engineers in speech and multimodal AI
• Support for publications at top-tier conferences and professional development
• Access to state-of-the-art GPU infrastructure for training large-scale audio models