استخدام (AI/Machine Learning Engineer (Q-Commerce در دیجی کالا

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استخدام (AI/Machine Learning Engineer (Q-Commerce در دیجی کالا

(2 days ago)
— • تهران، تهران
نوع همکاری
تمام وقت
سفرهای کاری
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دیپلم

شرح شغل و وظایف

دیجی کالا در تهران (محدوده ونک) جهت تکمیل کادر خود به افراد واجد شرایط ذیل نیازمند است.

Description job title
About the role
We build the intelligence layer behind a fast-moving q-commerce business — search and discovery, recommendations, delivery time prediction, demand signals, and LLM-powered assistants for customers and support agents. You'll own problems end to end: framing the business question, building the model, shipping it to production, and proving it moved a metric.
What you'll work on
Build and ship ML models on real Persian-language, high-volume transactional data: ranking and semantic search, recommendation, forecasting, ETA prediction, user segmentation and profiling.
Take models to production — feature pipelines, training/inference services, monitoring, retraining. Ownership doesn't end at the notebook.
Build LLM-based systems: retrieval-augmented pipelines, agentic workflows, embedding and vector search, evaluation harnesses to prove they actually work.
Design offline and online evaluation — A/B tests, ranking metrics (nDCG, MRR, recall@k), business KPIs — and be honest about what the numbers say.
Partner with product, commercial, and operations teams to translate ambiguous goals into modeling problems.
What we're looking for
3+ years building ML or data science systems that reached real users.
Strong Python and SQL. You're comfortable in a large relational warehouse and can write queries that don't melt it.
Solid grounding in ML fundamentals — you can explain why a model works, not just call .fit.
Experience deploying something: APIs, containers, batch pipelines, orchestration. You know the difference between a model and a service.
Clear communication. You can defend a design decision to an engineer and explain the tradeoff to a business stakeholder.
Strong pluses (any of these)
Search, ranking, or recommender systems at scale.
LLM application work: RAG, agent frameworks, prompt and eval engineering, fine-tuning.
Vector databases, embedding model evaluation, ASR/speech.
ClickHouse, Spark, Airflow, or similar data infrastructure.
How to think about fit
We're hiring across a spectrum. Some of you lean toward analysis and modeling (framing problems, experimentation, statistical rigor). Some lean toward ML engineering (pipelines, serving, latency, reliability). Some lean toward AI/LLM systems (retrieval, agents, evaluation). We're not looking for someone who is all three at once — tell us where your center of gravity is and where you want to grow.
AI/Machine Learning Engineer | Q-Commerce

 

شرایط احراز شغل

سن
18 - 50 سال
جنسیت
فرق نمی کند
تحصیلات
دیپلم

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