Junior 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 a Junior 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 contributions will help shape the future of commerce in the AI era.

Key Responsibilities:

• Own the End-to-End AI Lifecycle: Take ownership of designing, training, evaluating, and deploying ML/LLM-based systems, supported by senior team members to ensure success.
• Prototype to Production: Lead the transformation of AI research into scalable, maintainable production systems — balancing speed and quality while learning best practices.
• 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.
• Engineering Standards & Best Practices: Develop reliable, monitored, and testable ML pipelines and APIs, applying software engineering best practices to gain deeper expertise in MLOps.
• AI Platform Evolution: Contribute to shaping how AI is built and scaled across Yotpo by helping to improve shared infrastructure, tooling, and best practices.

What You Bring:

• BSc in Computer Science, Data Science, or a related field (MSc a plus), with academic or industry experience in AI/ML or applied data science.
• Experience (academic or practical) designing, training, and evaluating ML models — ideally in tasks like summarization, classification, personalization, or conversational agents.
• Familiarity with ML & GenAI frameworks such as:
  PyTorch, HuggingFace Transformers, LangChain, and vector databases (e.g. FAISS, Pinecone)
• Basic understanding of Big Data concepts & Streaming frameworks:
  Spark, Kafka (Flink is a plus)
• Interest in LLMOps workflows and tooling:
  AWS Bedrock, OpenAI APIs, Langfuse, LangSmith, MLFlow, Feature Stores
  Some experience is a plus
• Solid foundation in Python programming (object-oriented design and OOP patterns) and eagerness to deepen expertise through hands-on projects.
• Advanced familiarity with SQL for querying and managing data.
• Cloud infrastructure experience - huge advantage:
  AWS/GCP, Docker, Kubernetes, CI/CD, monitoring, observability
• Strong communication skills — able to explain complex topics simply and effectively.
• A researcher's mindset — self-driven curiosity, eagerness to learn, and passion for exploring emerging technologies.

Technical Skills:

• AI Model Design & Evaluation: Hands-on exposure to training models with academic or real-world datasets, selecting architectures, and applying evaluation metrics.
• LLM-Oriented Applications: Familiarity with prompt engineering, embeddings, retrieval-augmented generation (RAG), and early steps in fine-tuning LLMs.
• System Design for AI: Basic understanding of scalable AI system patterns, monitoring, and lifecycle management; motivated to apply and grow in this area.

Cloud & Infrastructure: Exposure to cloud platforms (AWS, GCP, Azure) and foundational infrastructure concepts such as containerization and deployment.

#LI-Hybrid