About The Role The role owns the end-to-end architecture and deployment of machine learning systems powering high-throughput production environments.
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. Key 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
- 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
- Bonus: Master's or PhD in Computer Science, Machine Learning, or a related technical field, along with contributions to open-source ML projects
Machine Learning Engineer in austin at Unknown Company
This position is listed as full time and onsite.