AI Jobs in Munich

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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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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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Lead, hire, and mentor a team of AI Deployment Engineers supporting Codex customers across strategic accounts; own the operating model and engagement strategy for Codex deployment efforts to ensure customers move from pilot to production adoption; guide teams in designing and implementing AI-enhanced development workflows, automations, and scalable deployment architectures; act as the senior technical escalation point for complex customer implementations and deployment challenges; partner with Sales, Product, Research, and Applied Engineering teams to align customer outcomes with product direction and roadmap priorities; help establish repeatable deployment playbooks, technical patterns, and best practices for scaled adoption of AI coding tools; coach engineers to serve as trusted advisors to engineering leadership and executive stakeholders; synthesize insights from customer deployments into actionable feedback for internal teams; champion safe, reliable, and effective adoption of AI-powered development workflows across industries.

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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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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This will involve understanding the AI systems built by the company, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This will involve understanding the AI systems built, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.