Description review
ML Engineer
Orcrist Technologies · Germany · back to the listing
HR standards
66/100
needs work
Title ↔ description
83/100
solid
Reads as
Machine Learning Engineer
100% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Company
Orcrist builds the Orcrist Intelligence Platform (OIP), a secure, Kubernetes-native data intelligence system deployed as SaaS or self-hosted/on-prem (including air-gapped missions). We fuse data processing, ML, and intuitive UX for defense, law-enforcement, and enterprise teams.
Role
Productionize the NLP/audio/document models that power OIP’s insight experiences. You’ll own model packaging, deployment, monitoring, and evaluation—partnering with Research and product squads to deliver trustworthy enrichment worldwide.
What you’ll do
• Package and deploy models (ASR, translation, OCR, NER, summarization) using Triton/KServe on Kubernetes.
• Build evaluation pipelines (WER, BLEU, F1, latency, cost) and automate release gating.
• Operate streaming + batch inference via Kafka, Temporal, and backfill tooling.
• Monitor drift/quality with Prometheus, Grafana, Evidently; optimize inference cost and performance.
• Collaborate with TypeScript teams on payload schemas, contracts, and human-in-the-loop feedback loops.
About you
• 4–8+ years ML engineering/MLOps, shipping models to production.
• Strong Python, PyTorch/Transformers, and experience with Triton/KServe or similar.
• Comfortable with Kubernetes, GitOps, CI/CD, and GPU workload operations.
• Knowledge of evaluation metrics, monitoring, and annotation workflows.
• Eligible to work in Germany; export-control screening required for certain programs.
Nice-to-haves
• Temporal, Beam/Flink, or Ray Serve experience; ONNX/TensorRT optimization.
• German language (B1+) and familiarity with defense or public safety datasets.
• WhisperX, DeepStream/GStreamer, or vector search integrations.
What we offer
• Modern MLOps stack: Triton, Temporal, Kafka, MLflow/Weights & Biases, Evidently, Kubernetes.
• Remote-first in Germany with regular Berlin meetups, 30 days vacation, equipment & learning budget.
Originally posted on Himalayas
Orcrist builds the Orcrist Intelligence Platform (OIP), a secure, Kubernetes-native data intelligence system deployed as SaaS or self-hosted/on-prem (including air-gapped missions). We fuse data processing, ML, and intuitive UX for defense, law-enforcement, and enterprise teams.
Role
Productionize the NLP/audio/document models that power OIP’s insight experiences. You’ll own model packaging, deployment, monitoring, and evaluation—partnering with Research and product squads to deliver trustworthy enrichment worldwide.
What you’ll do
• Package and deploy models (ASR, translation, OCR, NER, summarization) using Triton/KServe on Kubernetes.
• Build evaluation pipelines (WER, BLEU, F1, latency, cost) and automate release gating.
• Operate streaming + batch inference via Kafka, Temporal, and backfill tooling.
• Monitor drift/quality with Prometheus, Grafana, Evidently; optimize inference cost and performance.
• Collaborate with TypeScript teams on payload schemas, contracts, and human-in-the-loop feedback loops.
About you
• 4–8+ years ML engineering/MLOps, shipping models to production.
• Strong Python, PyTorch/Transformers, and experience with Triton/KServe or similar.
• Comfortable with Kubernetes, GitOps, CI/CD, and GPU workload operations.
• Knowledge of evaluation metrics, monitoring, and annotation workflows.
• Eligible to work in Germany; export-control screening required for certain programs.
Nice-to-haves
• Temporal, Beam/Flink, or Ray Serve experience; ONNX/TensorRT optimization.
• German language (B1+) and familiarity with defense or public safety datasets.
• WhisperX, DeepStream/GStreamer, or vector search integrations.
What we offer
• Modern MLOps stack: Triton, Temporal, Kafka, MLflow/Weights & Biases, Evidently, Kubernetes.
• Remote-first in Germany with regular Berlin meetups, 30 days vacation, equipment & learning budget.
Originally posted on Himalayas