Centific is looking to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers, and to help these organizations unlock significant business value by deploying GenAI at scale.
Requirements
- PhD candidate in CS/EE (or related) with research in speech, audio ML, or multimodal LMs.
- Fluency in Python and PyTorch, with hands‑on GPU training; familiarity with torchaudio or librosa.
- Working knowledge of modern sequence models (Transformers or SSMs) and training best practices.
- Depth in at least one area: (a) discrete speech tokens/temporal compression, (b) modality alignment to LLMs via adapters, or (c) post‑training/instruction tuning for speech tasks.
- Experience with speech generation (neural codecs/vocoders) or hybrid text+speech decoding.
- Background in multilingual or code‑switching speech and domain adaptation.
- Hands‑on work evaluating safety, bias, hallucination, or spoofing risks in speech systems.
Responsibilities
- End‑to‑end speech dialogue systems (speech‑in/speech‑out) and speech‑aware LLMs.
- Alignment between speech encoders and text backbones via lightweight adapters.
- Efficient speech tokenization and temporal compression suitable for long‑form audio.
- Reliable evaluation across recognition, understanding, and generation tasks—including robustness and safety.
- Latency‑aware inference for streaming and real‑time user experiences.
- Prototype a conversational SLM using an SSL speech encoder and a compact adapter on an existing LLM; compare against strong baselines.
- Create a data recipe that blends conversational speech with instruction‑following corpora; run targeted ablations and report findings.
Other
- Location: Redmond(Preferred) or Remote
- Duration: <3–6 months>
- Competitive stipend and hands-on projects with measurable real-world impact.
- Mentorship from applied scientists and engineers; opportunities to publish and present.
- Access to modern GPU infrastructure and a supportive environment for fast, responsible experimentation.
