AI Researcher​/ML Engineer – ASR, Speech Specialist

Responsibilities

• Architect, train, fine‑tune, and evaluate state‑of‑the‑art speech representations and ASR models (e.g., End‑to‑End Conformer, Whisper, RNN‑T, and hybrid CTC/Attention architectures) across multiple global languages.
• Design and deploy highly scalable algorithms for dynamic vocabulary insertion, contextual biasing, and language model personalization to precisely capture customer‑specific terminology, acronyms, and product names.
• Implement automated framework evaluations to benchmark model performance, rigorously tracking Word Error Rate (WER), Character Error Rate (CER), embedding‑based metrics, latency budgets (RTF), and computing efficiency profiles under varying acoustic environments.
• Develop pioneering multilingual benchmarks for end‑to‑end conversational AI agents, including speech‑to‑text and text‑to‑speech components.
• Partner with core engineering teams to build, optimize, and maintain high‑throughput pipelines optimized for both ultra‑low latency real‑time streaming inference and high‑efficiency asynchronous (batch) multi‑channel speech analysis.
• Translate product requirements into technical AI roadmaps, working hand‑in‑hand with Product Managers to ship speech‑to‑text, simultaneous translation, and semantic speech analytics features.

• Master’s or Ph.D. degree in Computer Science, Electrical Engineering, Computational Linguistics, Data Science, or related quantitative field with emphasis on speech processing or deep learning (or equivalent proven industry track record).
• Minimum of 3–5 years of dedicated professional experience developing ASR systems, speech‑to‑text translation pipelines, or advanced audio processing models.
• Advanced proficiency with PyTorch or equivalent frameworks, along with extensive experience utilizing dedicated speech toolkits such as Whisper, NVIDIA NeMo, Hugging Face Transformers, Kaldi, ESPnet, or Speech Brain.
• Hands‑on experience converting and running PyTorch models on at least one mobile inference runtime:
  Execu Torch, LiteRT (formerly Tensor Flow Lite), or ONNX Runtime Mobile.
• Strong software engineering principles in Python, with clear understanding of data structures, algorithm optimization, and handling complex multilingual text/audio tokenization schemas.
• Proven experience working with large‑scale audio datasets, audio augmentation techniques (e.g., Spec Augment, noise injection), and text normalization/inverse text normalization (ITN) pipelines.