About the job
As a Training Infrastructure Engineer, you'll design, develop, and maintain large-scale backend and cloud-native infrastructure to support distributed machine learning training, inference, and data processing pipelines for our generative AI platform. You'll architect scalable, resilient backend infrastructure, lead technical design discussions, mentor engineers, and establish best practices for large-scale machine learning systems.
Responsibilities
Architect and build scalable, resilient backend infrastructure to support distributed training, inference, and data processing pipelines
Lead technical design discussions, mentor engineers, and establish best practices for large-scale machine learning systems
Design and implement core backend services with a focus on efficiency and low latency
Drive infrastructure optimization initiatives for compute cost, storage lifecycle management, and network performance
Collaborate with machine learning, DevOps, and product teams to translate research and product requirements into robust infrastructure solutions
Evaluate and integrate cloud-native and open-source technologies such as Kubernetes, Ray, Kubeflow, and MLFlow to enhance platform reliability
Own end-to-end systems from design to deployment, emphasizing reliability, fault tolerance, and operational excellence
Qualifications
Minimum
Bachelor's degree or equivalent in Computer Science or related field plus four (4) years of experience in software engineering or related role
4 years of experience designing, building, and optimizing large-scale backend infrastructure and distributed data systems (e.g., PostgreSQL, MySQL, DynamoDB, Apache Spark, Apache Flink, Apache Kafka) in cloud environments (AWS, GCP, Azure, or equivalent), including cloud-native platforms, core infrastructure components, and optimization techniques (caching, indexing, sharding, replication, transactions, ACID)
4 years of experience with major server-side programming languages and frameworks (e.g., Python, C++, Go, TypeScript)
4 years of experience writing technical design documentation, leading cross-functional projects, and collaborating with cross-functional teams to achieve business impact
3 years of experience developing and maintaining data processing and API systems, including client-server communication frameworks (e.g., gRPC, Thrift)
3 years of experience conducting A/B testing and scientific experimentation (e.g., Statsig, Meta Deltoid, Optimizely) to measure software impact
3 years of experience conducting coding interviews and providing systematic feedback for engineering candidates
2 years of experience with cloud-native tools and infrastructure, such as Docker and Kubernetes
2 years of experience defining and implementing data-driven metrics to support company or team goals
Preferred
No preferred qualifications listed.