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 Experience: 3+ years of professional experience as a Machine Learning Engineer or similar role. Proven experience building and deploying ML models and/or LLM-based solutions into production. Technical Skills: Proficiency in Python and ML/AI frameworks like PyTorch, TensorFlow, or scikit-learn. Strong understanding of ML algorithms, model evaluation, feature engineering, and MLOps best practices. Experience working with and deploying LLMs (e.G., OpenAI GPT-4, Claude, LLaMA, or similar). Familiarity with prompt engineering, fine-tuning, RAG (retrieval-augmented generation), and agent frameworks. Experience with cloud platforms like Google Cloud Platform, Amazon Web Services, or Microsoft Azure. Experience with containerization and model serving (e.G., Docker, Kubernetes, TensorFlow Serving, FastAPI). Applied AI Experience: Strong background in supervised/unsupervised learning, predictive modeling, and experimentation. Experience with recommender systems, ad optimization models, or marketing intelligence. Familiarity with LLM integrations in production systems is a big plus. Soft Skills: Strong problem-solving abilities and ownership mindset. Excellent communication and collaboration skills. Comfortable in a fast-moving startup environment. Bonus Points Experience applying LLMs to real-world product use cases in e-commerce or marketing. Knowledge of agentic AI architectures and multi-model orchestration. Experience contributing to open-source ML/LLM projects. Familiarity with backend/frontend systems to integrate AI into customer experiences. Domain experience with e-commerce platforms (e.G., Amazon Seller Central). Why Join Us Build AI that directly impacts thousands of e-commerce businesses. Work on both ML and LLM innovation at the frontier of applied AI. Own your work end-to-end in a lean, ambitious team. Flexible work culture, high autonomy, and the chance to shape the future of our platform.