Machine Learning Engineer

Job description

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.

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.

• 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.
  
  

• 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.
  
  

• 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).
  
  

• 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.
  
  

Required Skill Profession

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