Machine Learning Engineering Manager

Role overview Guide machine learning work from early experiments through production, turning research and model ideas into useful product features. The role combines building models and data pipelines with integrating AI into an application, evaluating its behavior, and improving it in response to evidence and user feedback.

Responsibilities - Develop models, data pipelines, and AI features that help users explore ideas and find relevant information. - Integrate models with application services so they can support everyday workflows. - Move promising experiments into production, monitor model behavior, and refine systems over time. - Assess data and model quality, investigate errors, and make evaluation results clear to colleagues. - Collaborate with product, engineering, and security teams to prioritize work and learn from customer feedback.

Requirements - Experience building and operating machine learning systems, including data preparation, evaluation, and deployment. - Ability to work across product layers, from APIs and databases to user-facing interfaces. - Practical coding skills and interest in investigating model behavior and solving problems with colleagues. - Familiarity with model monitoring, language models, retrieval, and tools for operating AI services. - Curiosity about research and good judgment in translating it into product decisions. - Clear communication, collaborative working habits, and initiative in a remote team.

Benefits and work setup - Full-time, remote role. - Competitive compensation package.