Requirements
- 8+ years of relevant software or data engineering development experience in a fast-paced, high growth environment
- 3+ years of experience with machine learning infrastructure, MLOps, or ML platform engineering
- Proven track record of setting technical direction and leading complex, multi-team initiatives
- Strong programming and analytical ability with expertise in Python, Scala, or Java, and infrastructure-as-code
- Experience with feature store services and how they interoperate between batch and live inference systems
- Experience with live inference services including caching, SLAs, and performance optimization for production ML workloads
- Experience with model lifecycle management including versioning, A/B testing, and rollback capabilities
- Experience with streaming systems (Spark, Kafka) and how they relate to overarching ML platform architecture
- Experience with cloud-based ML platforms such as AWS SageMaker, Google Vertex AI, or Azure ML
- Experience mentoring engineers and establishing technical best practices across teams
- Staff-Level Capabilities
Nice to Have
- Experience with real-time recommendation systems and personalization platforms at scale
- Knowledge of ML model serving frameworks (TensorFlow Serving, TorchServe, Seldon, etc.)
- Experience with A/B testing frameworks and experimentation platforms
- Experience with distributed computing frameworks (Ray, Dask, etc.)
- Knowledge of ML security and privacy considerations
- Track record of technical writing or speaking at conferences about ML infrastructure
Work Arrangement
Hybrid
Additional Information
- The anticipated gross base pay range is below for this role. Actual compensation will vary depending on factors such as a candidate’s qualifications, skills, experience, and competencies. Base annual salary is one component of StubHub’s total compensation and competitive benefits package, which includes equity, 401(k), paid time off, paid parental leave, and comprehensive health benefits.
