Machine Learning Engineer

About the role

The 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.

Responsibilities

• Design and implement scalable machine learning pipelines using Python, PyTorch, and distributed data processing frameworks
• Deploy, monitor, and scale models in production using cloud infrastructure such as AWS or GCP
• Optimize model inference latency, memory footprint, and throughput for high-traffic endpoints
• Collaborate with data engineers to establish robust data quality checks across feature stores and training pipelines
• Conduct rigorous code reviews, establish engineering best practices, and contribute to system architecture discussions

Requirements

• 3 to 6 years of professional software engineering experience with at least 3 years dedicated to machine learning engineering
• Strong proficiency in Python and deep experience with PyTorch, TensorFlow, or equivalent ML frameworks
• Demonstrated production experience with containerization, orchestration, and MLOps tools like Docker, Kubernetes, and MLflow
• Solid foundation in software design principles, API development, and distributed computing

Qualifications

Bonus: Master's or PhD in Computer Science, Machine Learning, or a related technical field, along with contributions to open-source ML projects.