ML Engineer

Orcrist Technologies GmbH; United States; Remote; USD 130,000 - 170,000; Full time

Benefits offered by this job

30 days vacation

Equipment & learning budget

Remote-first in Germany with Berlin-me

Job summary

Orcrist Technologies GmbH is seeking an ML Engineer to productionize NLP/audio/document models powering OIP. You will own packaging, deployment, monitoring, and evaluation in collaboration with Research and product squads.

You will work on streaming and batch inference, model evaluation pipelines, and cost/performance optimization on Kubernetes-based infrastructure, with remote-first collaboration across teams in Europe and the US.

Qualifications

• 4-8+ years ML engineering/MLOps experience shipping models to production.
• Strong Python and PyTorch/Transformers expertise.
• Experience with Triton/KServe or similar.
• Comfortable with Kubernetes, GitOps, CI/CD, and GPU workload operations.
• Knowledge of evaluation metrics, monitoring, and annotation workflows.

Responsibilities

• Package and deploy models (ASR, translation, OCR, NER, summarization) on Kubernetes using Triton/KServe.
• 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.

Skills

Python

PyTorch/Transformers

Kubernetes

GitOps

CI/CD

GPU workloads

Evaluation metrics

Monitoring

Annotation workflows

Tools

Triton

KServe

Prometheus

Grafana

Temporal

Beam

Flink

Ray Serve

ONNX/TensorRT

Job description

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.