ML Engineer

What you'll do

• Build ML prototype vertical slices that connect ingest/processing to inference and visible product outcomes (search, insights, UX flows). - Create evaluation harnesses and decision artifacts: datasets, baselines, quality/latency/cost metrics, and go/no-go recommendations. - Package prototypes for adoption: containerize services, define reproducible deployments, and produce runbooks/checklists. - Partner with Research and Data Engineering on dataset curation, annotation loops, experiment tracking, and safe iteration. - Make prototypes operationally credible: instrumentation, monitoring, and security/compliance basics (PII handling, provenance mindset). ### Nice‑to‑haves

• GPU serving/optimization experience (Triton/KServe, ONNX/TensorRT, batching, quantization). - Streaming/pipeline tooling (Kafka, Ray, Beam/Flink/Spark) and search/vector/graph integrations. - German language (B1+) and/or experience with regulated/public-sector datasets and workflows. ### What We Offer

• Modern ML stack in real constraints: Kubernetes, streaming, and hybrid/on-prem/air-gapped deployments. - Remote-first in Germany with regular Berlin workshops, 30 days vacation, equipment & learning budget. - High leverage: your prototypes and handoffs unblock multiple delivery teams.