AI Research Engineer

If you're into online shopping (who isn't these days?), chances are you've crossed paths with Yotpo. We're all about eCommerce retention—helping brands of every size turn one-time shoppers into lifelong customers.

Think loyalty programs and reviews—it's what we do best. Plus, we've got more tricks up our sleeve.

With teams spread across the globe—from the US and Canada to the UK, Israel, Bulgaria, and Australia—we're growing fast. Our main mission? Delivering cutting-edge technology that sets new standards in the industry.

Sounds exciting? Then read on, because we’re looking for curious professional talents to be a part of building the future of the e-commerce industry.

Yotpo helps brands turn first-time shoppers into lifelong customers through reviews and loyalty.

Our technology is at the heart of it all. If you’re passionate about solving complex problems using advanced AI and ML — and building production-grade systems that redefine customer experiences — this could be your dream job.

Your Mission at Yotpo:

We are looking for an AI Research Engineer to join our Applied AI team — a highly skilled and collaborative group building end-to-end AI solutions powered by machine learning, LLMs, and cutting-edge architectures.

You’ll work closely with product, engineering, and data teams to explore, prototype, and productionize advanced AI capabilities in Yotpo’s UGC product line. From semantic search and summarization to personalized recommendations and chat agents — your work will shape the future of commerce in the AI era.

Key Responsibilities:

• Own the End-to-End AI Lifecycle: Design, train, evaluate, and deploy ML/LLM-based systems — from proof-of-concept to robust production infrastructure.
• Prototype to Production: Translate cutting-edge AI research into scalable and maintainable production-ready systems, balancing speed and quality.
• Business-Driven Problem Solving: Work closely with stakeholders to understand business needs, formulate clear objectives, and solve real-world problems using data and AI.
• Collaborative R&D: Partner with engineers, data scientists, product managers, and researchers to deliver cross-functional AI capabilities.
• Promote Engineering Standards: Build reliable, monitored, and testable ML pipelines and APIs, using software engineering best practices and MLOps principles.
• Mentorship & Technical Leadership: Share expertise, review designs, mentor teammates, and contribute to our growing knowledge base in AI and LLM systems.
• AI Platform Evolution: Help shape how AI is built, adopted, and scaled across Yotpo — including shared infrastructure, tooling, and best practices.
  
  

• MSc with 2+ years or BSc with 4+ years of experience in AI/ML engineering, applied data science, or related fields
• Production-grade Python skills and advanced SQL capabilities
• Proven experience designing, training, and deploying ML models. Including tasks such as: Summarization, Semantic Search, Classification, Personalization, Chat agents, etc.
• Strong knowledge of ML and GenAI frameworks such as: PyTorch, HuggingFace Transformers, Langchain, Vector Databases (e.g. FAISS, Pinecone)
• Familiarity with LLMOps tooling: AWS Bedrock, OpenAI APIs, Langfuse, LangSmith, MLFlow, Feature Stores
• Exposure to Big Data & Streaming: Spark, Kafka (Flink is a plus)
• Comfort with MLOps and cloud infrastructure: AWS/GCP, Docker, Kubernetes, CI/CD, monitoring, observability
• Understanding of architectural patterns for large-scale software systems, including modularity, fault-tolerance, scalability, and data flow across distributed environments
• Excellent communication skills, both technical and non-technical, with a strong ability to explain complex topics clearly
• Self-starter with a researcher mindset and a passion for exploring emerging technologies
  
  

• AI Model Design & Evaluation: Experience training models using real-world data, choosing appropriate architectures, and defining metrics for evaluation.
• LLM-Oriented Applications: Hands-on experience with prompt engineering, retrieval-augmented generation (RAG), embeddings, and fine-tuning LLMs.
• System Design for AI: Understanding of scalable AI architecture patterns, monitoring, and lifecycle management in production environments.
• MLOps & DevX: Build systems that are reproducible, observable, testable, and easy to evolve — including CI/CD, model versioning, and rollback strategies.