Staff Engineer, Machine Learning & Data Infrastructure

Who We Are

About the Opportunity

What You'll Be Doing

• Improve model inference latency, throughput, resource utilization, and cost. Profile the full prediction path -from feature retrieval and preprocessing to model execution - and deliver measurable improvements without unacceptable model-quality regressions.
• Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.
• Design and optimize offline data pipelines on Databricks, Apache Spark, and warehouse/lakehouse technologies, addressing data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost.
• Connect offline and online feature pipelines, ensuring point-in-time correctness, consistent feature definitions, freshness, lineage, and reproducible backfills.
• Evolve reliable feature services and data integrations supporting risk models and decision systems.
• Set technical direction, mentor engineers, and lead delivery across ML, data engineering, cloud infrastructure, security, and product teams.

What We Look For In You

• A record of building and operating large-scale distributed systems, ML infrastructure, or data platforms, with technical ownership across teams.
• Hands-on experience optimizing production model performance, supported by measurable improvements in latency, throughput, utilization, or cost.
• Deep understanding of Databricks and Spark, or comparable offline data platforms, including execution plans, distributed query optimization, storage layouts, data correctness, and workload cost management.
• Experience with the model lifecycle, including training/serving integration, validation, deployment, observability, and rollback.
• Strong Python, SQL, and at least one backend or systems language such as Java, Scala, Go, or C++.
• Ability to turn ambiguous requirements into clear architecture and deliver maintainable systems through hands-on engineering and technical leadership.

Nice to Haves

• SageMaker, Alibaba Cloud PAI/EAS, or comparable ML platforms.
• Inference optimization using tools such as ONNX Runtime, TensorRT, or Triton.
• Delta Lake, MLflow, feature stores, Kafka, Flink, and low-latency data stores.
• Fraud detection or financial risk systems.
• Fluency in Mandarin

Perks & Benefits

• Competitive total compensation package
• L&D programs for employees' growth and development
• Various team building programs and company events
• Wellness and meal allowances
• Comprehensive healthcare schemes for employees and dependants
• More that we love to tell you along the process!