Junior AI Engineer

Click Mint is a fast-growing eCommerce conversion optimization startup. We use AI-driven diagnostics, experimentation, and behavioral data to help modern brands unlock revenue already embedded in their traffic. Our work lives at the intersection of machine learning, product, and real-world business impact—not research for research’s sake.

This is an early-career role with meaningful exposure to production AI systems, experimentation infrastructure, and applied decision modeling, working closely with senior engineers and product leaders.

• Support the development and iteration of AI-driven features across diagnostics, experimentation, and analytics
• Work with structured and behavioral data to build, test, and refine models used in real customer environments
• Assist in training, evaluating, and deploying models used for prediction, classification, and optimization
• Collaborate with Product and Engineering to translate ideas into shippable, testable systems
• Help maintain data pipelines, feature extraction logic, and model evaluation workflows
• Participate in experimentation design, validation, and post-test analysis
• Contribute to internal tooling and prototypes that accelerate learning and deployment

• 0–3 years of experience in machine learning, data science, or applied AI roles
• Strong foundations in Python and modern ML frameworks (e.g., PyTorch, Tensor Flow, or similar)
• Familiarity with supervised learning, basic statistics, and model evaluation concepts
• Comfort working with real-world data (messy, incomplete, opinionated)
• Curiosity, strong problem-solving instincts, and a willingness to learn quickly
• Ability to communicate technical ideas clearly to non-ML teammates

• Experience with experimentation platforms, A/B testing, or causal inference concepts
• Exposure to LLMs, embeddings, or prompt-driven systems
• Familiarity with data tooling (SQL, dbt, Airflow, or similar)
• Interest in eCommerce, growth, or behavioral analytics

• Early-stage startup with real responsibility and mentorship—not “watch from the sidelines” work
• Hybrid culture with tight feedback loops between product, engineering, and business
• Hands-on exposure to production AI systems, not toy projects
• Opportunity to grow into a senior AI or applied ML role as the platform scales

If you’re excited about applying AI to real decisions—and seeing your work ship, learn, and compound—this is a strong place to start.