Description
Disney Entertainment & ESPN Technology is seeking a Senior Machine Learning Engineer to work on building and operating large-scale data infrastructure and real-time processing platforms that power next-generation personalization and live sports experiences.
The ideal candidate will have experience building and operating distributed data and ML infrastructure that supports high-throughput, low-latency data processing and real-time ML use cases. They will work closely with senior MLEs, data engineers, platform/SRE, and product teams to develop streaming data pipelines, feature computation systems, and ML-adjacent services that operate reliably at scale.
Responsibilities and Duties of the Role:
- Large-Scale Data Processing & Streaming Systems:
- Build and maintain high-throughput batch and streaming data pipelines to support ML, analytics, and real-time decisioning use cases.
- Implement data ingestion, enrichment, aggregation, and transformation workflows using modern distributed data frameworks.
- Ensure pipelines meet latency, reliability, and data quality requirements for downstream ML and product teams.
- Real-Time Data & Feature Infrastructure:
- Develop and operate systems that support real-time feature computation and delivery for online ML services.
- Work with feature stores and event-driven architectures to ensure consistency between offline and online data.
- Improve data freshness, schema evolution, and backward compatibility in streaming environments.
- ML-Adjacent infrastructure & Platform Engineering:
- Build and operate ML-adjacent services such as inference inputs, feature APIs, and data access layers.
- Contribute to scalable service patterns including autoscaling, rollout strategies, and resiliency mechanisms.
- Partner with platform/SRE teams to improve system availability, performance, and cost efficiency.
- Reliability, Observability & Operations:
- Instrument data and ML infrastructure with metrics, logging, and alerting to support production operations.
- Participate in on-call rotations and incident response for data and ML platforms.
- Identify and remediate data pipeline failures, performance regressions, and operational risks.
- Collaboration & Engineering Execution:
- Collaborate with applied ML and data science teams to enable production ML workflows through reliable data systems.
- Participate in design reviews, code reviews, and technical discussions.
- Follow established platform standards and contribute incremental improvements over time.
Required Education, Experience/Skills/Training:
- Basic Qualification:
- Experience building and operating large-scale data or ML systems in production.
- Strong fundamentals in distributed systems and data processing architectures.
- Hands-on experience with streaming and batch data technologies (e.g., Kafka, Kinesis, Spark, Flink, or equivalent).
- Proficiency in Python and working knowledge of Java, Scala, Go, or C++.
- Experience operating systems in cloud-native environments (AWS, containers, Kubernetes, IaC tools).
- Familiarity with observability and operational best practices for production systems.
- Strong collaboration skills and ability to work effectively across engineering and data teams.
- Preferred qualification:
- Experience supporting real-time personalization, recommendation, or analytics systems.
- Familiarity with feature stores, event-driven architectures, and real-time ML pipelines.
- Exposure to ML infrastructure concepts such as inference pipelines, data validation, and model lifecycle tooling.
- Experience optimizing data systems for latency, throughput, and cost efficiency.
- Understanding of experimentation platforms and data instrumentation for online systems.
The hiring range for this position in New York, NY is $150,900.00 - $202,400.00 per year, in Glendale, CA is $144,100.00 - $193,200.00 per year, and in San Francisco, CA $157,800.00 - $211,600.00 per year.