Machine Learning Engineer II

Job Posting Title:

Machine Learning Engineer II

Req ID:

10102658

Job Description:

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. Whether that’s evolving our streaming and digital products in new and immersive ways, powering worldwide advertising and distribution to maximize flexibility and efficiency, or delivering Disney’s unmatched entertainment and sports content, every day is a moment to make a difference to partners and to hundreds of millions of people around the world. A few reasons why we think you’d love working for Disney Entertainment & ESPN Technology

• Building the future of Disney’s media business: DE&E Technologists are designing and building the infrastructure that will power Disney’s media, advertising, and distribution businesses for years to come.

• Reach & Scale: The products and platforms this group builds and operates delight millions of consumers every minute of every day – from Disney+ and Hulu, to ABC News and Entertainment, to ESPN and ESPN+, and much more.

• Innovation: We develop and execute groundbreaking products and techniques that shape industry norms and enhance how audiences experience sports, entertainment & news.

Our team develops and maintains recommendation and personalization algorithms for Disney Streaming’s suite of streaming video apps, notably Disney+ and Hulu. As a member of this team you will collaborate across Engineering, Product, and Data teams to apply machine learning methods to meet strategic product personalization goals, explore innovative, cutting edge techniques that can be applied to recommendations, and constantly seek ways to optimize operational processes. This is an Individual Contributor role in content recommendations. You will be expected to lead recommendation and personalization algorithm research, development, and optimization for product areas, and to coordinate requirements and manage stakeholder expectations with Product, Engineering, and Editorial teams. You will be expected to help meet KPIs for product areas and to set and meet deadlines for external and internally facing tools, such as offline evaluation tools for pre-production algorithms. As an IC, you will also be responsible for helping to set the roadmap for algorithmic work — not only for how to approach product requests for new recommendation features, but for helping to drive larger company objectives in the areas of personalization and content recommendation.

Responsibilities:

• Algorithm Development and Maintenance: Utilize cutting edge machine learning methods to develop and implement algorithms for personalization, recommendation, and other predictive systems; maintain algorithms deployed to production and be the point person in explaining methodologies to technical and non-technical teams

• Feature Engineering and Optimization: Develop and maintain ETL pipelines using orchestration tools such as Airflow and Jenkins; deploy scalable streaming and batch data pipelines to support petabyte scale datasets

• Development Best Practices: Maintain existing and establish new algorithm development, testing, and deployment standards

• Collaborate with product and business stakeholders: Identify and define new personalization opportunities and work with other data teams to improve how we do data collection, experimentation and analysis

• Experience loading and querying cloud-hosted databases
• Building streaming data pipelines using Kafka, Spark, or Flink

Basic Qualifications:

• 3+ years of experience developing machine learning models, performing large-scale data analysis, and/or data engineering experience

• Bachelor’s degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience

• 3+ years writing production-level, scalable code (e.g. Python, Scala)

• 3+ years of experience developing algorithms for deployment to production systems

• In-depth understanding of modern machine learning (e.g. deep learning methods), models, and their mathematical underpinnings

• Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark

• Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick, effective solutions as appropriate

• Strong written and verbal communication skills

Preferred Qualifications:

• MS or PhD in statistics, math, computer science, or related quantitative field

• Production experience with developing content recommendation algorithms at scale

• Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment

• Familiar with metadata management, data lineage, and principles of data governance

#DISNEYTECH

The hiring range for this position in New York is $118,000.00-158,200.00 per year, in Los Angeles is $136,100.00-182,400.00 per year, in California is $123,400.00-165,400.00 per year and in Seattle is $118,000.00-158,200.00 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.

Job Posting Segment:

Product & Data Engineering

Job Posting Primary Business:

PDE - Engagement Experiences & Platforms

Primary Job Posting Category:

Machine Learning

Employment Type:

Full time

Primary City, State, Region, Postal Code:

New York, NY, USA

Alternate City, State, Region, Postal Code:

USA - CA - 2450 Broadway, USA - CA - Market St, USA - WA - 925 4th Ave

Date Posted:

2024-11-12