AI Jobs in Berlin

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Own the machine learning product roadmap for core speech and NLP across markets, turning field signals into clear priorities. Run a structured customer-feedback to ML loop with Sales, Customer Support, and Customer Success, including direct customer conversations and closing the loop back to the field. Define and maintain the model evaluation framework that gates releases, including metrics, slices, thresholds, and regression bars. Partner closely with Speech & NLP Leads to ship improvements to production. Improve product analytics and ML data integration by closing gaps between goals, production measurements, and model training data. Own ML product KPIs such as no-edit rate, documentation rate, edit rate per slot, and transcription accuracy, and drive measurable improvements.

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As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.

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Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.

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Utilize proprietary software to provide accurate input and labels for healthcare and administration projects, ensuring high-quality data for AI model training. Deliver curated, high-quality data for scenarios involving patient care coordination, medical billing, administrative workflows, and healthcare operations. Collaborate with technical staff to support the training of new AI tasks and contribute to the development of innovative technologies. Assist in designing and improving efficient annotation tools tailored for healthcare and administration data. Select and analyze complex problems in healthcare and administration fields aligned with your expertise to enhance AI model performance. Interpret, analyze, and execute tasks based on evolving instructions, maintaining precision and adaptability.

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Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.

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As the AI Enablement Engineer at voize, you will define, operationalise, and scale how artificial intelligence is adopted within the company. You will build the AI infrastructure by designing and maintaining voize's internal AI productivity suite, connecting agents, data, and workflows to enable cross-functional automation and decision-making. You will enable and educate the organisation by building AI fluency through onboarding sessions, workshops, playbooks, and function-specific best practices. You will set guardrails by establishing and enforcing standards for AI governance, safe use, data handling, and compliance in partnership with Security and IT. You will consult and collaborate with teams and leadership to embed AI into workflows, build agents and automations, and translate business needs into prioritised AI initiatives. You will drive change by managing the transition towards an organisation where humans and AI agents work side by side. You will analyze tool usage and adoption signals, spot fatigue patterns, and iterate on improvements that change outcomes. Additionally, you will help create and maintain a language model-readable context layer with a sensible update process so agents and humans operate on shared truth.

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Design computational material science problems to challenge a frontier AI model requiring specialized tools. Pick an anchor tool and design a problem based on its waveform-processing kernels, geophysical inversion routines, sub-surface flow solvers, or data pipelines. Write a Python reference solution, supply input files and model or domain definitions where needed. Decide the numerical answer and the tolerance for correctness. Test the problem against the AI model in batches of parallel attempts, tuning difficulty until the agent succeeds in a small number of attempts. Submit tasks for senior reviewer feedback to ensure quality. Tune problems iteratively based on AI performance to achieve a 10–30% pass rate, rewriting scenarios and adjusting parameters as needed while gaining expertise in both the tool and AI behavior.

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Design computational engineering problems to challenge a frontier AI model using specialized tools like Cantera, CoolProp, CalculiX, OpenFAST, or others installed in a sealed Linux container. Write Python reference solutions and supply necessary input files and definitions. Determine the numerical answer and appropriate domain-specific tolerance for correctness. Test and tune the problem difficulty against batches of parallel model attempts to achieve a pass rate between 10-30%. Submit tasks for review by a senior expert for feedback and quality assurance. Continuously refine problems by rewriting thermodynamic cycles, adjusting material models and boundary conditions, and analyzing model behavior through test attempts. Gain a deeper understanding of both the engineering tools and the AI model's approach to complex thermal, structural, and fluid mechanics problems.

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Build realistic consulting project environments by creating detailed project scenarios grounded in real engagement dynamics, including industry context, financials, constraints, conflicting inputs, and incomplete information. Design structured consulting tasks for AI agents by breaking projects into discrete tasks that mirror real consulting work such as market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, benchmarking, and more. Define evaluation criteria and quality standards by developing grading frameworks, evaluation rubrics, and golden-answer solutions for each task, which are used to train and calibrate an LLM-based grading system that evaluates AI outputs at scale. This role is remote, project-based, and focused on analytical design and evaluation as an individual contributor.

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AI Product Builders at n8n own one focused AI workstream where they are close to users, technology, and the future of AI products. They work either independently or alongside a senior PM as a mentor and collaborate closely with cross-functional teams including Engineering, Design, Data, Product Marketing, DevRel, Community, Support, and Customer Success. The role is not focused on ML platform or research but involves building with AI deeply through products, prototypes, public projects, agent workflows, or tools used by real users. Possible workstreams include AI TRUST, which focuses on improving AI quality, observability, reliability, and human-in-the-loop patterns to make AI work in production, and AI BUILDING / SUPER AGENT, which focuses on making n8n an AI-first product by shifting usage to AI-native interfaces and features such as Super Agent, custom memory, Skills, and proactive suggestions.