(Senior) Machine Learning Engineer at Orcrist Technologies

Job description

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. Role We are looking for a hands-on ML Engineer to build and productionize modern AI capabilities across text, vision, audio, 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 Data Engineering teams to take promising models and ideas from experimentation to reliable, production-ready systems. What you’ll do Evaluate, compare and integrate open-source models for concrete product and customer use cases. Build and improve LLM-based applications. Extend and improve AI and ML models using inference optimization, evaluation, and fine-tuning where appropriate. Work with NLP, translation, speech-to-text / ASR, image and 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 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. Experience with inference engines like vLLM or SGLang, and platforms like NVIDIA Triton or Ollama. Familiarity with fine-tuning, prompting, model evaluation, inference, and deployment. Working understanding of AI model encoding and quantization formats, and the tradeoffs thereof. Strong engineering mindset and ability to turn experiments into reliable, reproducible systems. Eligible to work in Germany; export-control screening required for certain programs. Nice-to-haves Hands-on experience with model serving and production deployment, using technologies such as Kubernetes, KServe, NVIDIA Triton, and/or Ray Serve. Knowledge of inference optimization techniques, including batching, quantization, ONNX, or TensorRT. German language skills (B1+) and/or familiarity with defense or public safety datasets. 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 Remote-first, Germany-wide: Work from wherever you do your best work, with regular team gatherings in Berlin and other off-site locations. Flexibility by default: Flexible working hours help you make work fit your life. Your setup, your way: Get a personal home-office equipment budget to create a workspace that works for you. 30 days of vacation: Take the time you need to recharge and come back with fresh energy. Keep growing: We invest in your personal and professional development. Get rewarded for impact: Performance bonuses are tied to agreed objectives and key results. A warm welcome: Every new team member gets a welcome goodie bag. Good people, good times: From summer and Christmas parties to regular team gatherings, we make time to celebrate together. A mission that matters: Work on challenges with tangible impact on public safety and national security.

Requirements

Must-haves

What you’ll do
Evaluate, compare and integrate open-source models for concrete product and customer use cases.
Build and improve LLM-based applications.
Extend and improve AI and ML models using inference optimization, evaluation, and fine-tuning where appropriate.
Work with NLP, translation, speech-to-text / ASR, image and 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 while remaining hands-on with models and model behaviour.
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.
Experience with inference engines like vLLM or SGLang, and platforms like NVIDIA Triton or Ollama.
Familiarity with fine-tuning, prompting, model evaluation, inference, and deployment.
Working understanding of AI model encoding and quantization formats, and the tradeoffs thereof.
Strong engineering mindset and ability to turn experiments into reliable, reproducible systems.
Eligible to work in Germany; export-control screening required for certain programs.

Nice-to-haves

Hands-on experience with model serving and production deployment, using technologies such as Kubernetes, KServe, NVIDIA Triton, and/or Ray Serve.
Knowledge of inference optimization techniques, including batching, quantization, ONNX, or TensorRT.
German language skills (B1+) and/or familiarity with defense or public safety datasets.
Exposure to geospatial AI, satellite imagery, or remote sensing.
Experience working in constrained or regulated environments with infrastructure, security, or deployment requirements.

Application prep

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Keywords to mirror

kubernetesmlaillmmachine learningpython

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Vision

Professional Development