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Disney Entertainment and ESPN Product & Technology
Senior Machine Learning Engineer
Disney Entertainment and ESPN Product & Technology
New York•On-site•5-8 Yrs•3 hours ago
Job description
Overview
Disney Entertainment & ESPN Technology On any given day at Disney Entertainment & ESPN Technology, we’re reimagining ways to create magical viewing experiences for the world’s most beloved stories while also transforming Disney’s media business for the future.
Responsibilities
- 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
Qualifications
- 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++.
- Familiarity with observability and operational best practices for production systems.
- Strong collaboration skills and ability to work effectively across engineering and data teams
- 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.
- Understanding of experimentation platforms and data instrumentation for online systems.
- 5+ years of industry experience building data‑intensive or ML‑adjacent systems in production
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field