ML engineers manage the full lifecycle of ML model development, from design and training to deployment and maintenance, while ensuring seamless integration into production use cases
Job Summary
ML engineers manage the full lifecycle of ML model development, from design and training to deployment and maintenance, while ensuring seamless integration into production use cases.
ML engineering ensures that the models shaping our liquidity offerings, member safety, and efficient operations are not just innovative but operationally robust and fully aligned with business objectives.
ML engineers act as technical ambassadors, clearly communicating complex concepts and collaborating across product, data, and business domains.
Matching Summary
ML engineers manage the full lifecycle of ML model development, from design and training to deployment and maintenance, while ensuring seamless integration into production use cases.
Skills & Requirements
Must-have
production ML systems at scale
distributed systems engineering
ML frameworks (SageMaker, Vertex AI, Kubeflow)
cloud platforms (AWS, GCP, Azure)
modern MLOps practices
Nice-to-have
business acumen
technical ambassadors
resilient, high-performance infrastructure
Key Requirements
5+ years of experience in engineering
2+ years in ML engineering
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field
Strong understanding of data privacy, security, and compliance