ML Engineer

Role overview Hands-on machine learning engineer role at a data intelligence platform company, focused on productionizing AI capabilities across text, vision, audio, and other applied ML use cases. The position emphasizes evaluating and adapting state-of-the-art and open-source models, rather than training foundation models from scratch or only building pipelines.

Responsibilities - Evaluate, compare, and integrate open-source models for concrete product and customer use cases - Build and improve LLM-based applications spanning NLP, translation, speech-to-text, image, and document understanding - Design evaluation frameworks covering model quality, latency, reliability, and cost - Take models from experimentation into production, including packaging, deployment, monitoring, and iteration - Optimize inference performance and operational cost - Collaborate with Research and Product teams to turn prototypes into scalable product capabilities, while contributing to modern ML infrastructure and MLOps

Requirements - 4+ years in Machine Learning Engineering, Applied AI, or a similar hands-on ML role - Strong Python skills with practical use of PyTorch, Transformers, and Hugging Face - Experience working with LLMs, NLP, speech, or other modern generative AI systems - Hands-on experience evaluating and experimenting with existing models, not only building infrastructure - Experience with inference engines such as vLLM or SGLang and platforms like NVIDIA Triton or Ollama - Familiarity with fine-tuning, prompting, model evaluation, inference, and deployment - Working understanding of model encoding and quantization tradeoffs - Eligible to work in Germany; export-control screening required for certain programs

Nice to have - Hands-on model serving and production deployment with Kubernetes, KServe, NVIDIA Triton, or Ray Serve - Knowledge of inference optimization techniques such as batching, quantization, ONNX, or TensorRT - German language skills at B1+ or familiarity with defense or public safety datasets - Exposure to geospatial AI, satellite imagery, or remote sensing - Background working in constrained or regulated environments with strict infrastructure, security, or deployment requirements

Benefits and work setup - Remote-first position based in Germany with regular Berlin meetups - 30 days of vacation - Equipment and learning budget - Close collaboration between Research, Product, and Engineering teams on real-world product use cases