{"id":48783,"url":"https://alion.io/job/centific-ai-research-engineer-speech-1","title":"AI Research Engineer- Speech 1","company":{"id":9945,"name":"Centific","domain":"centific.com","url":"https://alion.io/company/centific","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"C","score":62,"open_postings":95,"ghost_share":0.632,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Redmond, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":160000,"currency":"USD","period":"year","gross":null,"usd_annual":160000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Chain-of-Thought","optional":false},{"name":"Edge AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Tokenization","optional":false},{"name":"Transformers","optional":false},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"DeepSpeed","optional":true},{"name":"FastAPI","optional":true},{"name":"FSDP","optional":true},{"name":"Gemini","optional":true},{"name":"gRPC","optional":true},{"name":"Hallucination","optional":true},{"name":"Hugging Face","optional":true},{"name":"LLM Evaluation","optional":true},{"name":"NVIDIA NeMo","optional":true},{"name":"ONNX","optional":true},{"name":"PEFT","optional":true},{"name":"Quantization","optional":true},{"name":"Qwen","optional":true},{"name":"TensorRT","optional":true},{"name":"Weights & Biases","optional":true},{"name":"Whisper","optional":true}],"status":"live","first_seen_at":"2026-07-21T00:00:00Z","employer_posted_date":"2026-07-21","last_verified_at":"2026-10-18T05:49:41Z","board_verified":true,"closed_at":null,"days_open":89,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":89},"description":"About Centific\nCentific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem-comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets-to create contextual, multilingual, pre-trained datasets; fine-tuned, industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation™ solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster.\nOur mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets.\nAbout Job\nJob Description\nAI Engineer: Speech/Audio\nCentific AI Research\nAbout Centific AI Research\nCentific AI Research is at the forefront of developing cutting-edge AI solutions that bridge the gap between research innovation and real-world applications. Our team of scientists and engineers work collaboratively to create impactful technologies across speech, audio, and multimodal AI domains. We are committed to building responsible AI systems that deliver measurable impact while maintaining the highest standards of research quality.\nThe Opportunity\nWe are seeking an AI Engineer: Speech/Audio to join our growing team and drive innovation in next-generation audio AI technologies. This role focuses on Large Audio Language Models (LALMs), Large Audio Reasoning Models, and Speech-to-Speech (S2S) systems that can understand, reason over, and generate audio with human-like capabilities.\nYou will work at the intersection of cutting-edge research and production systems, developing Spoken Language Models (SLMs) that perform complex audio reasoning and engage in natural speech-based interactions. This position offers the opportunity to shape our technical direction in audio-native AI while collaborating with world-class researchers and engineers.\nKey Responsibilities\nDesign, develop, and deploy Large Audio Language Models (LALMs) capable of native audio understanding, reasoning, and generation.\nBuild Large Audio Reasoning Models that perform complex chain-of-thought reasoning over speech and audio inputs, including medical, technical, and conversational domains.\nContribute to Speech-to-Speech (S2S) system development, including speech understanding, dialogue management, and speech synthesis components.\nResearch and implement alignment mechanisms between speech encoders and LLM backbones using lightweight adapters, LoRA, and efficient fine-tuning strategies.\nDesign efficient speech tokenization and temporal compression techniques suitable for long-form audio reasoning and multi-turn spoken dialogue.\nBuild comprehensive evaluation frameworks for audio reasoning capabilities, including benchmarks for speech QA, audio understanding, and reasoning accuracy.\nOptimize inference pipelines for low-latency, streaming applications in speech systems.\nCollaborate with cross-functional teams to transfer research innovations into production systems and customer-facing applications.\nContribute to technical documentation, research write-ups, and publications at top-tier venues (NeurIPS, ICML, ACL, Interspeech).\nMinimum Qualifications\nMaster'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.\n2+ years of industry or applied research experience in speech/audio AI, Large Language Models, or multimodal systems.\nDemonstrated applied research contributions through publications, patents, or shipped products in speech/audio AI or LLMs.\nStrong proficiency in Python and PyTorch, with hands-on experience in GPU-accelerated training for large-scale models.\nSolid understanding of speech and audio signal processing, acoustic modeling, and audio representations.\nWorking knowledge of modern LLM architectures (Transformers, SSMs) and training paradigms including instruction tuning and alignment methods.\nFamiliarity with modality alignment techniques: adapter-based integration, cross-modal attention, or audio-text fusion methods.\nStrong experimentation habits: clean code, systematic ablations, reproducibility, and clear technical communication.\nPreferred Qualifications\nPublication record at top-tier venues (NeurIPS, ICML, ICLR, ACL, Interspeech, ICASSP) in audio language models, speech reasoning, or multimodal learning.\nHands-on experience building or fine-tuning Large Audio Language Models (e.g., Qwen-Audio, SALMONN, LTU, Gemini Audio).\nExperience with speech representation pretraining (HuBERT, Wav2Vec 2.0, Whisper, WavLM) and discrete speech tokenization.\nFamiliarity with Speech-to-Speech components: neural audio codecs (EnCodec, SoundStream), vocoders, or speech synthesis systems.\nExperience with audio reasoning benchmarks (AIR-Bench, MMAU, AudioBench) or building evaluation harnesses for audio QA.\nHands-on experience with distributed training (FSDP, DeepSpeed) and inference optimization (ONNX, TensorRT, quantization).\nFamiliarity with speech frameworks such as ESPnet, SpeechBrain, NVIDIA NeMo, or Fairseq.\nExperience with multilingual speech systems, code-switching, or domain adaptation for specialized applications (medical, legal, technical).\nBackground in evaluating safety, bias, hallucination, or adversarial robustness in audio language models.\nTechnical Environment\nCore: PyTorch, CUDA, torchaudio/librosa, Hugging Face Transformers\nLLM Stack: Large language model backbones, lightweight adapters (LoRA, Q-Former), instruction tuning pipelines\nAudio Models: Neural audio codecs, speech encoders, vocoders, discrete speech tokenizers\nInfrastructure: Modern GPU clusters, experiment tracking (Weights & Biases), distributed training frameworks\nDeployment: FastAPI/gRPC for services, ONNX/TensorRT for optimized inference\nWhat We Offer\nCompetitive compensation package with comprehensive benefits\nOpportunity to work on cutting-edge Large Audio Language Models and audio reasoning research with real-world impact\nCollaboration with experienced applied scientists and engineers in speech and multimodal AI\nSupport for publications at top-tier conferences and professional development\nAccess to state-of-the-art GPU infrastructure for training large-scale audio models\nFlexible work arrangements with hybrid/remote options\nLocation\nRedmond, WA / Palo Alto, CA / Remote\nSalary: $150-$160k Annually.\nCentific 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. 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