Machine Learning Engineer
We are founder-led and at our size that means you're in the room with them, on the calls, in the decisions, shaping the product and the pitch. As one of our first 50 employees, you'll play a crucial role in building on our culture rather than inheriting one. We argue to learn, not to be right. We ship fast, because working beats perfect. When we lose, we learn from it; when we win, we win together.
Your title is where you start, not where you stay, and there's room to grow into the work you're drawn to. Our team is 50/50 men and women with over 12 languages spoken.
If that sounds like your kind of team, we'd love to meet you.
We are seeking a highly skilled, hands-on Machine Learning Engineer who thrives as a generalist to help us scale our production AI capabilities in complex, real-world retail environments. Rather than a theoretical research position, this is a highly applied engineering role where you will focus on building, debugging, and shipping production-ready models and robust data pipelines.
We value breadth over depth in any single domain — specialized areas like computer vision are easy enough to pick up on the job. Working closely with a high-caliber team of engineers and data scientists, your practical execution will directly influence Wisy’s core platform, data flows, and future trajectory.
• Train, debug, and ship any kind of model to solve practical retail data challenges — model type matters less than your ability to get it into production.
• Build, maintain, and optimize robust data engineering foundations, including scalable ETL/ELT pipelines.
• Take end-to-end ownership of data quality, data cleaning, and preprocessing workflows to ensure pristine model inputs.
• Build efficient pipelines for model inference and postprocessing, collaborating closely with backend and infrastructure engineers.
• Implement best practices in MLOps, including version control, model monitoring, reproducibility, and automated deployment pipelines.
• Own model evaluation using performance metrics to ensure stability and accuracy in production environments.
• Support internal tooling and improve the developer experience as it relates to model deployment and data visualization.
• Diagnose, troubleshoot, and optimize performance issues within existing production models and data pipelines.
• Create and maintain technical documentation and support onboarding of new team members.
• Contribute to sprint planning, estimations, and architecture discussions.
• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.3+ years of hands-on software or data engineering experience with an emphasis on production machine learning.
• Strong proficiency in training models with PyTorch or TensorFlow.Excellent programming skills in Python and strong database capabilities in SQL.
• Proven experience building operational data pipelines (ETL/ELT) and managing data quality frameworks.
• Solid understanding of MLOps concepts, containerization (Docker, Kubernetes), and cloud environments (AWS, GCP).
• Fluent in async collaboration and Agile development practices.
• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
• 3+ years of hands-on software or data engineering experience with an emphasis on production machine learning.
• Strong proficiency in training models with PyTorch or TensorFlow.Excellent programming skills in Python and strong database capabilities in SQL.
• Proven experience building operational data pipelines (ETL/ELT) and managing data quality frameworks.
• Solid understanding of MLOps concepts, containerization (Docker, Kubernetes), and cloud environments (AWS, GCP).
• Fluent in async collaboration and Agile development practices.
• Experience with Computer Vision architectures, object detection models, or YOLO frameworks.
• Experience deploying models to resource-constrained edge or mobile environments.
• Familiarity with image annotation tools (e.g., CVAT, Label Studio) and labeling workflows.
• Experience working specifically within the retail, logistics, or consumer goods industries.
Link to portfolio (if applicable)
