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Required Machine Learning Platform Engineer
Team Overview
The ML Platform team builds the core infrastructure, services, and systems that enable machine learning at scale across the company.
We own the full lifecycle of ML workloads – from research and experimentation to reliable, large-scale production deployment.
The team focuses on:
Building scalable backend and infrastructure solutions for end-to-end ML workflows
Developing core ML platform components, including data processing, training, evaluation, and deployment
Empowering data scientists and product teams with robust, observable, and high-performance ML infrastructure
Solving complex challenges around scale, latency, throughput, reliability, and cost efficiency
Continuously evolving the ML platform by adopting new technologies and improving system performance and stability
What will you do?
As a ML Platform Engineer, you will design and build core backend systems and ML infrastructure that power research, training, and online inference at scale.
You will work at the intersection of backend engineering, data engineering, and machine learning, partnering closely with data scientists to transform research needs into production-grade platform capabilities with real business impact.
Key Responsibilities
Design, build, and own backend systems that support ML workflows, data processing, and core platform services
Architect and optimize systems for performance, scalability, reliability, and cost efficiency in production ML environments
Collaborate closely with data scientists to implement, iterate, and evolve ML platform capabilities
Build and maintain large-scale data ingestion, processing, and training pipelines
Ensure systems are production-ready through testing, observability, monitoring, and performance optimization
Investigate and resolve infrastructure and performance issues affecting critical ML and business KPIs.