Sr Machine Learning Engineer

The Walt Disney Company
New York, United States of America
4 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
$ 190K

Job location

New York, United States of America

Tech stack

Artificial Intelligence
Airflow
Amazon Web Services (AWS)
Unit Testing
Google BigQuery
Cloud Computing
Code Review
Continuous Integration
Data Cleansing
Data Integrity
Distributed Computing Environment
Python
Machine Learning
Open Source Technology
Cloud Services
Standard Sql
Software Deployment
SQL Databases
Feature Engineering
PyTorch
Snowflake
Spark
Deep Learning
Model Validation
Data Strategy
Containerization
Kubernetes
Information Technology
Kafka
Machine Learning Operations
Software Coding
Software Version Control
Data Pipelines
Docker
Unsupervised Learning
Databricks

Job description

The cross-media measurement and advanced analytics organization is responsible for data strategy & management, cross-platform content measurement, Content marketing measurement, and linear and digital inventory forecasting. The team provides advanced analytics and actionable insights related to Disney entertainment's content, monetization, and audience development., The Senior Machine Learning Engineer serves as an individual contributor responsible for leading end-to-end development of machine learning solutions, from data and feature design through model deployment and monitoring. This role applies machine learning techniques in code (e.g., supervised/unsupervised learning, classification/regression, clustering, and deep learning where appropriate) to develop systems that predict outcomes at scale for identity, audience, and cross-platform measurement use cases. The position includes building scalable ML pipelines and the data foundations required to capture, manage, store, and utilize large-scale structured and unstructured datasets, ensuring data integrity and interoperability across systems., * Develop, train, and deploy ML models for audience identity, look-alike modeling, and cross-platform measurement (including deep learning where appropriate); translate algorithms and technical specs into clean, testable Python/SQL code; containerize workloads via Docker/Kubernetes.

  • Design and own scalable ML data and feature pipelines using orchestration tools (Airflow/Dagster) to capture, validate, and deliver cross-media datasets across distributed cloud and/or platform environments.
  • Feature engineering & data preparation: develop reusable feature sets, manage metadata/lineage, and optimize storage/performance in Snowflake or Databricks to support training and inference.
  • MLOps & monitoring: implement CI/CD, model versioning/registry patterns, automated evaluation, and drift detection; build dashboards/alerts to ensure model reliability, reproducibility, and data quality in production.
  • Stakeholder collaboration & experimentation: lead offline/online experiment design, interpret results, and translate findings into actionable product enhancements for analytics, product, and engineering teams.
  • Data privacy & governance compliance: apply GDPR/CCPA principles, enforce PII safeguards, and contribute to documentation and audit readiness.
  • Team enablement: mentor junior engineers through code reviews and design reviews; share best practices and reusable tooling.

Requirements

Must have Production experience with deep-learning, genAI , or retrieval-augmented systems (PyTorch, vector databases) and real-time data pipelines (Kafka, Pub/Sub, Kinesis)

  • Must have at least 5 years of professional experience in machine learning engineering delivering production-grade models and ML pipelines at scale

  • Must have advanced coding skills in Python and SQL; strong software-engineering best practices (version control, CI/CD, unit testing, code reviews)

  • Must have demonstrated experience applying ML techniques in code to develop predictive systems (supervised/unsupervised learning; deep learning where appropriate)

  • Hands-on experience with cloud-native data platforms and distributed processing (Snowflake/Databricks/Spark/BigQuery) and orchestration (Airflow/Dagster)
  • Experience with containerization and production deployment patterns (Docker/Kubernetes) and operational monitoring

Preferred Qualifications:

  • 5+ years total experience, with hands-on work in media, advertising technology, or cross-platform audience measurement
  • Familiarity with modern MLOps stacks (e.g., MLflow, Kubeflow, Vertex AI, SageMaker) and model-governance practices (metadata, lineage, drift detection)
  • Certifications such as Google Professional Machine Learning Engineer, AWS Certified Machine Learning - Specialty, or equivalent cloud/data credentials
  • Contributions to open-source ML or data-engineering projects, conference presentations, or peer-reviewed publications

Required Education:

  • Bachelor's degree in a relevant technical or science field (e.g. computer science, data science, mathematics, or a related discipline)

Preferred Education:

  • Master's degree or PhD in a relevant field (e.g., Applied Math, Computer Science, Computational Science, Operation Research, Data Science)

Benefits & conditions

The hiring range for this position in New York City is $148,700.00 - $190,000.00 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.

About the company

The Walt Disney Company is an equal opportunity employer. Applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Disney fosters a business culture where ideas and decisions from all people help us grow, innovate, create the best stories and be relevant in a rapidly changing world.

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