Member of Technical Staff

Responsibilities

• Design, develop, and maintain large-scale backend and cloud-native infrastructure to support distributed machine learning training, inference, and data processing pipelines for generative AI platform.
• 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.

Requirements

• 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.

Compensation & Benefits

• Base Pay Range: $175,000–$220,000 USD (plus equity)
• Total compensation includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package.