About the Role

We’re looking for a Machine Learning Engineer who thrives on end-to-end ownership of machine learning systems — from problem framing and model development to deployment and monitoring in production. Unlike traditional roles that split responsibilities between Data Scientists and Engineers, this position is designed for hands-on builders who can independently develop, validate, and scale models that deliver real business impact.

Responsibilities

Design, train, and evaluate models independently using structured and unstructured datasetsBuild scalable and robust ML pipelines for training, validation, and continuous deliveryDevelop and own production-grade model serving systems, including monitoring and retraining workflowsWrite clean, maintainable code and establish patterns and best practices for ML developmentCollaborate with stakeholders to define success metrics and measure model impactLead or contribute to architectural decisions and long-term technical strategy

QualificationsMinimum

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Applied Math, or related field3+ years of hands-on experience building ML models independently and deploying them to productionDeep understanding of machine learning algorithms and statistical modeling techniquesProficiency in Python and ML frameworks (e.g., scikit-learn, PyTorch, XGBoost, TensorFlow)Experience with the full ML lifecycle, including model training, validation, and serving Solid engineering skills, including writing reusable code, working with APIs, and CI/CDFamiliarity with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)

Preferred:

Identify opportunities to apply machine learning and data-driven solutions across product and platform areasExperience with online model inference and real-time decision systemsExposure to feature engineering at scale and feature store designKnowledge of model observability, performance tracking, and drift detectionExperience deploying models using FastAPI, Flask, TorchServe, or equivalent