Jobs Hiring Near Me: Curated Data Scientist Jobs in Germany

This is a hands-on engineering role at an early-stage fintech company building an AI-powered platform for the tax advisory industry. You'll design and own the agentic AI foundation that underpins real product features — not prototypes — and ship those features directly into live advisory workflows where they make an immediate difference.

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This role offers the opportunity to build production-grade AI systems in a high-impact healthcare environment where reliability, evaluation, and safety are essential. You’ll own machine learning initiatives end-to-end, taking complex problems from early exploration through deployment and continuous improvement.

This is an entry-level AI Engineer position at a robotics startup in Munich, Germany. You'll work on developing and deploying deep learning models for industrial robots.

Join a cross-functional team at the intersection of AI, engineering, and laboratory automation to build agentic ML systems that reason, plan, and act inside real materials discovery workflows. This is a high-ownership role at a seed-stage deeptech startup operating in the AI-driven materials science and cleantech space, based in Berlin, Germany (on-site). Your work will enable robust, uncertainty-aware decisions that accelerate scientific progress while keeping humans meaningfully in the loop.

This role offers an opportunity to combine strong Java engineering expertise with hands-on Generative AI development. You will design and implement practical, production-ready AI solutions aligned with business and executive priorities.

The company is seeking an experienced AI/ML Engineer to join their Central Data Platform team, focusing on machine learning and GenAI infrastructure. The role involves designing, building, and maintaining end-to-end ML pipelines in Vertex AI, developing GenAI capabilities including embeddings, retrieval pipelines, vector databases, and RAG frameworks for chatbots, personalization, and semantic search, and implementing continuous evaluation, drift detection, performance monitoring, rollback strategies, and retraining triggers for deployed models. The candidate should have at least 5 years of experience in Data Engineering or ML Ops with a focus on productionising ML pipelines.

Join an early-stage industrial robotics startup in Munich, Germany. This entry-level AI Engineer position focuses on developing and deploying deep learning models for robotic work cells that operate in real industrial environments.

The role involves transforming complex, regulated accounting and advisory workflows into scalable, reliable software through AI-powered automation. The candidate will work closely with founders and product teams to design and ship high-impact features that make a real difference across partner firms.

Design and implement agentic systems that plan, reason, and act across real materials discovery workflows. Build decision-making systems that operate over experiments, simulations, and scientific datasets. Select next actions under uncertainty and encode when autonomy should act versus when a human should stay in the loop. Implement planning, control logic, and uncertainty-aware decision-making tailored to physical systems and lab environments. Encode operational, experimental, and safety constraints directly into agent behavior. Define stopping criteria, fallback strategies, and recovery mechanisms to prevent brittle behavior. Collaborate with AI researchers to embed predictive models into agent workflows and translate model outputs into executable actions. Integrate agents with laboratory automation and software systems so agent outputs drive real-world actions. Instrument agents with logging, monitoring, and diagnostics to support observability and debugging. Build evaluation frameworks that assess decision quality, learning efficiency, and system behavior — beyond simple model accuracy. Analyze failure cases and iterate on system design based on real-world experimental outcomes. Own systems end-to-end: from prototype through deployment and ongoing operation. Required 4–8 years of experience building ML-driven or algorithmic decision-making systems in production or applied research settings. Strong background in scientific or structured data modeling (rather than language-first or NLP-heavy systems). Experience with planning, control, optimization, probabilistic reasoning, or decision-making under uncertainty. Proficiency in modern ML frameworks such as PyTorch or JAX, paired with strong general software engineering skills. Comfortable owning systems end-to-end, from early prototype through to reliable production operation. Ability to reason clearly about system behavior in complex, partially observable environments. Clear communicator who can collaborate effectively across AI, engineering, and scientific teams. English fluency (additional language skills a plus). Nice to Have Technical curiosity about physical systems, laboratory experiments, and real-world constraints. Experience in materials science, chemistry, cleantech, or adjacent scientific domains. Familiarity with laboratory automation or robotics integration. Additional European language skills (German in particular). This role is on-site in Berlin, Germany. We work closely as a team in person, and we expect this role to be based full-time at our Berlin office. Visa sponsorship is not available.