DeepL - (Senior) Staff Research Scientist
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Requirements
• Deep expertise in speech, audio, or multilingual ML, particularly in ASR, MT, TTS, end-to-end ST, or large speech models. • A hands-on builder who enjoys training models, running experiments, debugging pipelines, and integrating ML systems into production. • Strong understanding of real-time streaming constraints and how to design models that operate reliably at low latency. • Experience shipping ML models to production, maintaining them at scale, and working with engineers on deployment, monitoring, and serving. • Ability to lead complex research efforts while staying grounded in product impact, user experience, and real-world performance. • Strong coding and experimentation skills (Python, PyTorch/JAX, audio processing libraries). • Ability to communicate clearly, collaborate across teams, and align research work with product and engineering priorities. • Proven experience mentoring others and elevating technical quality across a fast-moving, applied research team.
Responsibilities
• Lead hands-on research and development across ASR, MT, TTS, and speech-to-speech translation for real-time voice products. • Design, train, and optimize large-scale ASR models for multilingual accuracy, robustness, and ultra-low-latency streaming. • Improve cascaded translation pipelines end to end: segmentation, ASR→MT interfaces, streaming MT inference, and incremental decoding. • Develop and refine real-time TTS models with natural prosody, stable speaker characteristics, and fast inference. • Build and experiment with end-to-end and LLM-based speech-to-speech translation systems, including streaming and one-shot approaches. • Own the full lifecycle of model delivery: prototyping, ablations, training, evaluation, optimization, and production deployment. • Work closely with engineering teams to integrate models into real-time systems, ensuring reliability, uptime, and quality at scale. • Drive improvements in inference efficiency, model serving, voice UX, and robustness to real-world acoustic conditions. • Establish strong practices for evaluation, reproducibility, monitoring, and continuous model improvement in production. • Mentor researchers and engineers, promote hands-on collaboration, and raise the bar for model quality and operational excellence.
Benefits
• Remote work options are mentioned when discussing what sets DeepL apart and life at DeepL, suggesting that remote work is an available option for employees; however, no explicit details about how often or under which conditions this applies (e.g., percentage of days allowed to be worked remotely) are provided in the excerpt given: REMOTE WORK OPTIONS STATED • Given that none of these specific compensation benefits were explicitly stated within the job posting, and considering only what is contained within this text for your answer.
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