AI Research Engineer- Speech 1

Centific Global Solutions, Inc.; Redmond (WA); Hybrid; USD 150,000 - 160,000; Full time

Benefits offered by this job

Competitive compensation

Hybrid/Remote options

GPU infrastructure access

Support for conferences/publications

Job summary

Centific Global Solutions, Inc. is seeking researchers to design and deploy Large Audio Language Models with native audio understanding and reasoning capabilities. You will build reasoning models for multi-domain speech inputs and contribute to S2S systems, including dialogue management and synthesis.

The role emphasizes research in alignment, tokenization, evaluation, and low-latency pipelines, with opportunities to publish and collaborate across teams.

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.

Responsibilities

• 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 across 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).

Skills

Python

PyTorch

GPU training

Speech/Audio ML

Transformers

Experimentation

Education

Master's degree in CS/EE or related field

Ph.D. preferred

Tools

ONNX

TensorRT

DeepSpeed

Weights & Biases

Job description

Key Responsibilities

• 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 across 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, Wav2Vec2.0, 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.
• Flexible work arrangements with hybrid/remote options.

Location: Redmond, WA / Palo Alto, CA / Remote

Salary: $150‑$160k Annually.

Centific is an equal‑opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, citizenship status, age, mental or physical disability, medical condition, sex (including pregnancy), gender identity or expression, sexual orientation, marital status, familial status, veteran status or any other characteristic protected by applicable law. We consider qualified applicants regardless of criminal histories, consistent with legal requirements.