The position is for a Staff Machine Learning Scientist located in Germany, United Kingdom, Ireland, Denmark, Poland, or Portugal.
This role focuses on conversational AI, aiming to shape the future of intelligent customer interactions.
The candidate will join a senior-level team that bridges cutting-edge ML research with real-world product impact.
Responsibilities include driving innovation, leading rapid prototyping, and defining research practices.
The role involves mentoring scientists and engineers, owning end-to-end projects, and staying updated on ML and NLP advancements.
The candidate will foster collaboration by peer-reviewing research and sharing knowledge across teams.
Requirements:
A proven track record in Machine Learning or NLP research, ideally with senior industry experience is required.
Deep expertise in designing and implementing neural network architectures, particularly in NLP, is necessary.
Strong background with Generative AI, including LLMs, prompt engineering, and context management for AI agents is essential.
Experience with conversational AI and voice technologies such as TTS or STS is highly desirable.
Excellent collaboration and communication skills are required, with the ability to translate complex ideas for both technical and non-technical stakeholders.
A degree in Computer Science, Machine Learning, Statistics, Engineering, Mathematics, or a related field is mandatory.
Demonstrated ability to mentor peers, influence strategy, and contribute to a culture of innovation is expected.
Benefits:
The position offers a competitive compensation package with performance-based incentives.
Flexible hybrid or remote work arrangements are available across multiple European locations.
Comprehensive health and wellness coverage is provided.
Learning and development programs are available to support career growth.
The company promotes an inclusive culture with a strong commitment to diversity, equity, and inclusion.
Opportunities to work with cutting-edge AI technologies and influence product strategy at scale are offered.