Company
Orcrist is building a next generation data intelligence platform using cutting-edge technologies. We’re handling petabyte-scale data with sub-second queries. Our product is a Kubernetes-based platform delivered as B2B SaaS or as a self-hosted on-prem solution, including air-gapped deployments. We enable customers across defense, law enforcement, and enterprise to turn mission-critical data into actionable intelligence by fusing data processing, ML, and intuitive UX.
Role
We are looking for a hands-on ML Engineer to build and productionize modern AI capabilities across language, audio, documents, and other applied ML use cases. You will work directly with state-of-the-art and open-source models — testing, evaluating, optimizing, and fine-tuning them for real product use cases. This is not a role focused on training foundation models from scratch or building data pipelines only. You will work closely with Research, Product, and Engineering teams to take promising models and ideas from experimentation to reliable, production-ready systems.
What you’ll do
- Evaluate and compare open-source models for concrete product and customer use cases.
- Build and improve LLM-based applications, including prompt engineering, inference optimization, evaluation, and fine-tuning where appropriate.
- Work with NLP, translation, speech-to-text / ASR, document understanding, and related applied AI models.
- 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 and experiments into scalable product capabilities.
- Contribute to modern ML infrastructure and MLOps where needed — while remaining hands-on with models and model behaviour.
About you
- 4+ years of experience in Machine Learning Engineering, Applied AI, or a similar hands-on ML role.
- Strong Python skills and practical experience with modern ML frameworks and libraries such as PyTorch, Transformers, and Hugging Face.
- Experience working with LLMs, NLP models, speech models, or other modern generative AI systems.
- Hands-on experience evaluating and experimenting with existing models rather than only building ML infrastructure.
- Familiarity with fine-tuning, prompting, model evaluation, inference, and deployment.
- Strong engineering mindset and ability to turn experiments into reliable, reproducible systems.
- Comfortable working across the full lifecycle: experimentation, evaluation, implementation, and production.
- Eligible to work in Germany; export-control screening required for certain programs.
Nice-to-haves
- German language skills (B1+) and/or familiarity with defense or public safety datasets.
- Hands-on experience with model serving and production deployment, using technologies such as Kubernetes, KServe, Triton, or Ray Serve.
- Knowledge of inference optimization techniques, including batching, quantization, ONNX, or TensorRT.
- A strong understanding of model evaluation, monitoring, and experiment tracking.
- Exposure to geospatial AI, satellite imagery, or remote sensing.
- Experience working in constrained or regulated environments with infrastructure, security, or deployment requirements.
What we offer
- The opportunity to work hands-on with modern AI and open-source models.
- A modern ML stack and real-world product use cases.
- Close collaboration between Research, Product, and Engineering.
- Remote-first in Germany with regular Berlin meetups, 30 days vacation, equipment & learning budget.