Role OverviewWe are looking for a Machine Learning Engineer to join our team as a Member of Technical Staff. You will work on real-world production ML systems, building and improving core machine learning components across data, training, evaluation, and inference.This is an excellent opportunity for an engineer who wants to strengthen their ML systems skills by building, shipping, debugging, and continuously improving production models.Key ResponsibilitiesBuild and improve ML components across data, training, evaluation, and inference.Train, fine-tune, and adapt machine learning models for production use cases.Develop evaluation and testing processes to measure model performance and behavior.Build and maintain data pipelines using real-world and synthetic data.Debug model issues, performance bottlenecks, and production incidents.Optimize ML systems for accuracy, latency, cost, reliability, and safety.Deploy and maintain ML models running on GPU-based infrastructure.Collaborate closely with senior ML engineers, product teams, and research teams.Ship iterative improvements based on measurable results and real user feedback.Required SkillsStrong foundation in Machine Learning and modern neural network architectures.Strong programming skills in Python.Hands-on experience with PyTorch or JAX.Experience training, fine-tuning, or deploying ML models.Understanding of production ML systems and GPU-based environments.Ability to write clean, maintainable, production-quality code.Strong debugging and problem-solving skills.Ability to work effectively in ambiguous and fast-paced environments.Preferred QualificationsExperience with deep learning / neural networks.Experience building ML training pipelines and inference systems.Experience with model evaluation, testing, and performance optimization.Exposure to large-scale or production ML systems.Strong learning mindset and ability to quickly adopt new tools and technologies.Bias toward shipping, experimentation, iteration, and continuous improvement.Expected OutcomesML models consistently meet accuracy, latency, reliability, and performance targets.Production issues are identified, debugged, and resolved efficiently.Training pipelines, data pipelines, and inference systems are robust, reproducible, and maintainable.ML improvements are driven by measurable results and real-world feedback.Effective collaboration with engineering, product, and research teams to deliver reliable ML-powered features.
4 days ago