Staff Research Scientist focused on leading scientific innovation for DeepL's core text translation products. The role involves defining long-term scientific strategy, prototyping, running large-scale experiments, and driving breakthroughs into production. Responsibilities include designing, training, and optimizing LLM-based translation models, owning the full lifecycle of model delivery (prototyping, training, evaluation, optimization, deployment), and working with engineering teams on integration, reliability, and quality at scale. The role also emphasizes improvements in inference efficiency, model serving, voice UX, and robustness, as well as establishing practices for evaluation, reproducibility, monitoring, and continuous improvement. Mentoring researchers and engineers is also a key aspect.

Senior Staff Research Scientist at DeepL focusing on voice translation. The role involves leading research and development in ASR, MT, TTS, and speech-to-speech translation, with a focus on real-time, low-latency streaming systems. Responsibilities include designing, training, and optimizing large-scale models, improving cascaded translation pipelines, developing TTS models, building end-to-end systems, and owning the full lifecycle from prototyping to production deployment. The role also requires close collaboration with engineering, driving inference efficiency, and establishing best practices for evaluation and monitoring.

DeepL is seeking a Senior Staff Research Scientist to lead the design, implementation, and deployment of cutting-edge research in reinforcement learning and post-training at scale for large language models. The role involves shaping scientific strategy, building and deploying RL pipelines, post-training multi-modal models for alignment and general capabilities, and driving the research-to-production lifecycle. The ideal candidate has a PhD, 5+ years of ML research experience, strong expertise in deep reinforcement learning, and hands-on experience scaling and deploying foundation models.

Research Manager for a team focused on post-training foundation models for translation products, using techniques like reinforcement learning. The role involves people leadership and technical direction, balancing product commitments with research bets, and staying updated with literature to integrate cutting-edge ideas. The goal is to keep DeepL's language AI capabilities ahead of general-purpose models in customer-relevant domains.

AI Research Manager to lead the Translation Quality Research team, focusing on advancing and defending core translation models. The role involves shaping research agendas, prioritizing research, defining strategic goals, building models for human review estimation, and leading/developing a team of research scientists. The emphasis is on measurable quality and real-world production impact to maintain market dominance against large general-purpose models.