AI/ML Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI/ML Engineer based in Germany.

Join a mission-driven engineering team developing practical AI systems that address real-world challenges in healthcare, agriculture, education, and international development. As an AI/ML Engineer, you will design, build, evaluate, deploy, and continuously improve AI-powered applications used by nonprofit organizations and their communities. You will work on solutions ranging from clinical copilots and AI coaching tools to agricultural advisory chatbots and evaluation infrastructure. Collaborating with behavioral scientists, researchers, and embedded engineers, you will translate applied AI research into reliable, scalable products and reusable technical resources. You will also help partner organizations strengthen their own AI capabilities through mentorship, open-source contributions, and knowledge sharing. This fully remote role offers substantial technical ownership and the opportunity to apply advanced AI engineering to initiatives that can improve outcomes for millions of people.

Accountabilities:

In this role, you will build and improve production-ready AI systems while helping nonprofit partners adopt effective, measurable, and responsible AI practices. Your work will combine hands-on engineering, applied research, experimentation, and collaboration with teams working directly in the field.

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Design, build, evaluate, deploy, and improve machine learning systems and AI-powered applications for real-world use cases.

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Contribute to open-source AI evaluation infrastructure and tools that help nonprofit organizations assess and improve their AI products.

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Work closely with behavioral scientists, researchers, and field teams to identify recurring challenges and translate findings into reusable tools, frameworks, playbooks, and technical publications.

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Develop rigorous evaluation methods for AI systems, including appropriate datasets, evaluation criteria, and measurable performance improvements.

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Build and iterate on production-grade LLM applications and agentic systems, improving their reliability, usefulness, and performance for intended users.

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Release meaningful technical innovations from partner projects as open-source contributions whenever appropriate.

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Deliver solutions through rapid development and deployment cycles, using experimentation and measurable feedback to guide improvements.

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Document technical decisions, maintain engineering standards, and ensure AI components remain reliable and maintainable.

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Monitor developments in applied AI research, assess relevant advances, and test promising approaches in practical settings.

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Advise and support nonprofit organizations developing generative AI products, helping them adopt effective engineering practices, tools, and workflows.

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Mentor partner organization staff and help strengthen their internal technical capabilities and responsible AI practices.

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Contribute to workshops, seminars, and knowledge-sharing initiatives that communicate lessons and best practices from ongoing projects.

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Conduct site visits to understand operational challenges, observe how AI tools are used, and recommend technical improvements based on direct user needs.

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Take initiative in identifying problems, defining solutions, and driving projects through implementation and deployment.

Requirements:

The ideal candidate combines strong machine learning fundamentals with substantial hands-on experience delivering end-user AI products. You should be comfortable moving between research, experimentation, production engineering, and collaboration with nontechnical stakeholders, while maintaining a strong focus on practical impact.

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Strong foundations in mathematics, deep learning, large language models (LLMs), and modern AI systems.

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At least 4 years of hands-on machine learning engineering experience, including building, evaluating, and systematically improving at least one end-user-facing product powered by deep learning. Experience limited to integrating model APIs, building internal dashboards, or contributing solely to ML infrastructure does not meet this requirement.

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At least 1–2 years of experience building LLM-powered applications, including at least one production-grade agent beyond a proof of concept.

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Practical experience evaluating AI systems, with a strong understanding of dataset design, evaluation methodology, and performance measurement.

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Proficiency in Python and a willingness to remain deeply involved in hands-on implementation.

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Fluency in prompting and context engineering for agentic systems, including the ability to determine whether a problem requires improved prompting, a different model, better tools, or changes to the overall pipeline.

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Experience using AI coding agents to produce high-quality work, with the judgment and accountability to validate outputs and take responsibility for what you ship.

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Ability to read research papers, assess new ideas, and quickly test relevant approaches through practical experimentation.

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An experimental mindset, with the ability to reason carefully about data, models, and evaluation while making iterative, measurable progress.

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Strong first-principles thinking and the ability to design practical, scalable solutions.

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A user-focused approach that prioritizes whether a product works effectively for its intended audience.

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Proactive ownership, urgency, attention to detail, and the ability to carry projects through to completion without constant direction.

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Strong written and verbal communication skills, including the ability to explain technical concepts clearly to nonengineering audiences.…

Source: Arbeitnow (https://www.arbeitnow.com/jobs/companies/jobgether/ai-ml-engineer-germany-63592)