Machine Learning Engineer

About The RoleThe role owns the end-to-end architecture and deployment of machine learning systems powering high-throughput production environments.The engineering team works at the intersection of applied research and scalable backend systems, ensuring models operate with strict latency and reliability guarantees.Key ResponsibilitiesDesign and implement scalable machine learning pipelines using Python, PyTorch, and distributed data processing frameworksDeploy, monitor, and scale models in production using cloud infrastructure such as AWS or GCPOptimize model inference latency, memory footprint, and throughput for high-traffic endpointsCollaborate with data engineers to establish robust data quality checks across feature stores and training pipelinesConduct rigorous code reviews, establish engineering best practices, and contribute to system architecture discussionsWhat We Are Looking For3 to 6 years of professional software engineering experience with at least 3 years dedicated to machine learning engineeringStrong proficiency in Python and deep experience with PyTorch, TensorFlow, or equivalent ML frameworksDemonstrated production experience with containerization, orchestration, and MLOps tools like Docker, Kubernetes, and MLflowSolid foundation in software design principles, API development, and distributed computingBonus: Master's or PhD in Computer Science, Machine Learning, or a related technical field, along with contributions to open-source ML projects