Machine Learning Engineer

About Us

We're a small but ambitious startup revolutionizing the e-commerce landscape with cutting-edge AI solutions. Our mission is to empower sellers with the tools and insights they need to thrive in the competitive online marketplace.

We’re building a suite of AI-powered products and intelligent AI agents designed to streamline advertising, automate research, optimize listings, boost sales, and drive growth for businesses of all sizes.

The Role : Senior Machine Learning Engineer (AI / ML & LLMs – E-Commerce Ads)

We’re looking for a Senior Machine Learning Engineer to join our team and play a key role in developing the intelligence behind our AI-driven e-commerce platform.

In this role, you’ll be responsible for designing, building, training, optimizing, and deploying machine learning and LLM-based models that power ad targeting , recommendation systems , personalization engines , performance prediction , and autonomous AI agents .

You’ll own end-to-end ML pipelines — from data ingestion and feature engineering to model development , evaluation , and deployment — while also working hands-on with large language models to build intelligent workflows, tools, and automation.

Responsibilities

• ML & AI Model Development : Build, train, evaluate, and optimize ML models for ad performance prediction, personalization, targeting, and campaign optimization.
• Experiment with classical ML and modern deep learning architectures to maximize performance.
• LLM Development & Integration : Fine-tune, prompt-engineer, or build applications using large language models (e.g., OpenAI GPT-4, Claude, LLaMA).
• Build intelligent agent workflows using LLMs for research automation, ad copy generation, optimization strategies, and seller assistance.
• Integrate LLMs with internal data pipelines and APIs to deliver context-aware insights.
• Data Pipelines & Feature Engineering : Design and implement scalable data pipelines for training and inference.
• Process structured and unstructured e-commerce ad data for ML and LLM applications.
• Model Deployment & Infrastructure : Deploy ML and LLM models to production using containerization, inference APIs, or orchestration frameworks.
• Collaborate with backend teams to ensure seamless interaction between AI systems and product interfaces.
• Experimentation & Evaluation : Design and run experiments (e.g., A / B testing) to evaluate and iterate on models.
• Monitor model performance post-deployment and implement retraining or optimization strategies.
• Architecture & Scalability : Contribute to the design of AI infrastructure supporting rapid experimentation and reliable deployment at scale.
• Research & Innovation : Stay current on the latest advancements in ML, LLMs, and agentic AI systems.
• Propose and prototype new capabilities to strengthen our product’s intelligence.

Qualifications

Bonus Points

Why Join Us