Über
Adapt and fine-tune open-source foundation models (Llama, Mistral, and similar) and stand up production-grade RAG (Retrieval-Augmented Generation) pipelines that extract detailed product attributes and produce copy that drives conversion. Production-Scale ML Architecture:
Build systems that balance modern model accuracy with the cost, latency, and throughput realities of real-time e-commerce. You'll have strong instincts for when an LLM is the right call and when a tuned classifier, custom embeddings, or specialized vector retrieval is the smarter choice. Semantic Search and Retrieval:
Develop and refine vector-based search systems that connect shopper language to merchant catalog language with sub-second performance. Evaluation and Production Health:
Track live model performance, design robust evaluation frameworks for generative output (with a focus on minimizing hallucinations within retail taxonomies), and tune inference for high-traffic workloads. Cross-Functional Collaboration:
Work side by side with data engineering, MLOps, and product to move experimental prototypes into reliable, scalable, revenue-generating production services. What We're Looking For Education:
Bachelor's, Master's, or PhD in Computer Science, Data Science, Mathematics, Statistics, or another quantitative discipline. Experience:
2–5 years building, shipping, and maintaining ML models in production environments. Technical Stack:
Strong Python and SQL fundamentals. Deep working knowledge of modern deep learning and NLP tooling (PyTorch, Hugging Face, transformers). Familiarity with LLM deployment and orchestration frameworks (vLLM, LangChain, and fine-tuning approaches like LoRA / QLoRA). Hands-on experience with vector databases (Pinecone, Milvus, Weaviate) and embedding workflows. Pragmatic Architect:
Highly analytical, with demonstrated judgment about when generative AI is worth the cost and complexity and when classical ML is the better answer. Nice to Have:
Background in e-commerce, search relevance, recommendation engines, or working with large-scale retail data. Why This Role Scale and Impact:
You'll be working against a proprietary dataset that simply doesn't exist elsewhere, and your work will move real revenue numbers for major brands. Momentum:
The company is fast-growing and increasingly recognized as a category leader in retail AI. Team and Culture:
Collaborative, curious, and built around experimentation, diverse perspectives, and solving meaningful real-world problems. Compensation and Benefits:
Competitive salary, equity participation, full healthcare coverage, and flexible time off.
Sprachkenntnisse
- English
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