AI Jobs in Berlin

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As an AI Tutor in Mathematics, you will generate prompts that challenge AI, define comprehensive scoring criteria to evaluate the accuracy of the AI's answers, and correct the model's responses based on your domain-specific knowledge. You will contribute to projects aligned with your skills by creating training prompts and refining model responses to help shape the future of AI and ensure technology benefits everyone.

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Developing and maintaining MCP-compatible evaluation servers, implementing logic to check agent actions against scenario definitions, creating or extending tools that writers and QAs use to test agents, working closely with infrastructure engineers to ensure compatibility, and occasionally helping with test writing or debug sessions when needed.

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As a Senior AI Engineer, you will drive intelligent features that redefine how users build automations by building AI-powered capabilities such as natural language input and smart suggestions to make creating workflows faster and more intuitive. You will collaborate across engineering, product, and design to integrate generative AI and LLM-based enhancements into the core user experience, improve existing AI integrations, develop new ones, and shape how AI powers the product. Responsibilities include architecting and implementing AI-powered capabilities like code generation, intelligent node creation, and workflow optimization; integrating LLM APIs and embedding models for text-to-workflow and natural language code suggestions; designing and iterating on prompts to improve model output and user experience; building internal tooling, evaluation benchmarks, and automated testing for AI components; collaborating closely with other engineers to ensure AI features are reliable, performant, and scalable; balancing experimentation with impact by shipping quickly with a focus on user value; and staying current with advances in LLMs, prompt frameworks, and developer tools to inform the product roadmap.

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Lead customer discovery and design sessions to map business processes, identify automation opportunities, and define solution architecture. Design, build, and deploy integrations using low/no-code platforms (Zapier, Make, n8n, Workato) and CRM automation tools (HubSpot Workflows, Salesforce Flow) with API connectors. Collaborate with Engineering to validate technical feasibility, resolve blockers, and share field learnings that inform product improvements. Configure and optimize the AI Agent defining intents, prompts, actions, guardrails, and performance metrics. Manage complex, cross-functional deployments by defining timelines, aligning stakeholders, ensuring accountability, and delivering on time and within scope. Create scalable models and reusable frameworks (templates, playbooks, reference architectures) that make future projects faster and more consistent. Champion continuous learning and enablement by training peers, running internal workshops, and documenting best practices to raise the technical bar across the team. Run global, targeted outbound campaigns within the existing customer base to generate pipeline and accelerate adoption, working closely with the customer marketing team. Collaborate with GTM leadership to embed routines and cadences that drive accountability for new product pipeline, forecast accuracy, and performance tracking. Own regional top-line targets for assigned products by collaborating with Account Executives and Account Managers who hold add-on quotas. Act as an internal product owner within the GTM function by defining product-specific MRR strategies, coordinating cross-functional support, and ensuring Aircall delivers the leading AI-enabled communication platform. Collaborate with Product and PMM to shape the AI Voice Agent roadmap based on customer needs, integration insights, and field learnings. Drive internal and external product education, including enablement for System Integrators and channel partners. Maintain deep awareness of AI and CX industry trends, ensuring Aircall's positioning remains competitive and insights continuously feed back into product and GTM strategies.

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As an Applied Research Scientist at Superhuman, the role involves developing state-of-the-art tools for correcting, improving, and enhancing written English using various NLP, machine learning, and deep learning technologies. The individual will productize and ship these features into Superhuman's product offerings used by millions daily. They are expected to stay up-to-date with the latest research trends that could improve the product, contribute to the company's research strategy and technical culture, and attract professionals to build a best-in-class research team for the writing and communication assistant product.

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The responsibilities include evaluating what AI models produce related to the field of food preparation and serving work, assessing content related to the field of work, delivering clear and structured feedback to improve the AI model's understanding of workplace tasks and language, developing prompts for AI models that reflect the field, and evaluating AI responses. The work is performed remotely and asynchronously with flexible hours, and involves leveraging professional experience in food preparation and serving supervision to train AI models.

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Invent and prototype new model architectures that optimize inference speed, including on edge devices; build and maintain evaluation suites for multimodal performance across a range of public and internal tasks; collaborate with the data and infrastructure teams to build scalable pipelines for ingesting and preprocessing large audio datasets; work with the infrastructure team to optimize model training across large-scale GPU clusters; contribute to publications, internal research documents, and thought leadership within the team and the broader ML community; collaborate with the applied research and business teams on client-specific use cases.