Glendale, CA

Data Jobs in Glendale, CA

9 jobs found

Include remote jobs

Today

Lead Machine Learning Engineer - News

Disney Entertainment & Sports LLC Glendale, CA

Glendale, CA

Today

$142k - $190k/yr Full time Ai Ml Artificial Intelligence Aws Data Engineering

Apply Now

Job Description

Disney Entertainment & ESPN Technology is seeking a Lead Machine Learning Engineer to lead the News ML team in Glendale, CA. The role focuses on shaping technical direction and delivering scalable end-to-end machine learning for real-time, personalized news experiences across web, mobile, and connected TV.

Role Responsibilities

Lead complex technical initiatives end-to-end, spanning technical design, production deployment, and operational excellence.
Design and build infrastructure for the full machine learning lifecycle, including data pipelines and workflow orchestration, data discovery and quality tooling, and feature libraries.
Deliver data and ML solutions across diverse engineering scenarios, including recommendation systems, object detection, autogenerated tagging solutions, and RAGs.
Collaborate with product, editorial, and engineering stakeholders to translate business requirements into durable technical implementations.
Set technical priorities and manage workstreams to focus on high-impact, time-sensitive outcomes, while proactively identifying, communicating, and mitigating risks.
Promote engineering best practices across code quality, testing, CI/CD, observability, and incident response.
Mentor and coach engineers to strengthen ownership, collaboration, and continuous improvement.
Support technical documentation and facilitate knowledge sharing across teams.

Required Qualifications

Bachelor’s degree in computer science, Information Systems, Statistics, Math, or a comparable field of study, and/or equivalent work experience.
7+ years of software engineering experience.
5+ years of hands-on experience developing and deploying machine learning systems in production.
Expertise in data science, deep learning algorithms, or statistical methods used to address practical engineering problems.
Comfort working across the predictive stack, including data collection, data analysis, feature engineering, batch training, and low-latency online serving.
Experience designing and developing backend microservices for large-scale distributed systems using REST.
Cloud infrastructure experience, preferably AWS (Step Functions, Lambda, Glue, SQS, SNS, Personalize).
Experience building and deploying Spark and machine learning pipelines.
Hands-on big data experience with technologies such as Databricks, Kinesis, and Kafka.
Demonstrated leadership, coaching, and mentoring experience to help a team achieve business goals.
Experience with observability tools for metrics, logging, and monitoring such as Datadog.
Experience working in Agile/Scrum development environments.
Strong communication skills and a commitment to collaboration in a fast-paced, guest-focused environment.

Location and Employment Details

Location: Glendale, CA (onsite)
Salary Range: USD 141,900 - 190,300 per year
Experience Level: 7+ years

Technical Stack

AWS: Step Functions, Lambda, Glue, SQS, SNS, Personalize
Backend and ML Infrastructure: REST, Spark, Databricks
Data Streaming and Big Data: Kinesis, Kafka
Observability: Datadog

View full job page →

Today

Principal Machine Learning Engineer

Disney Entertainment & Sports LLC Glendale, CA

Glendale, CA

Today

$211k - $296k/yr Full time Manager Ai Ml Analytics

Apply Now

Job Description

Lead the technical direction of Disney’s News and Entertainment machine learning platform, spanning architecture, reliability, and production impact.

Responsibilities

Own end-to-end ML platform architecture for News and Entertainment across a broad portfolio including ABC News, ABC Entertainment, National Geographic, Marvel, and Disney Studios; author architecture documents, drive review, and oversee implementation to keep solutions scalable, reliable, and aligned with platform standards.
Identify and prioritize high-impact ML initiatives across the N&E portfolio; scope workstreams, sequence complex efforts, surface risks to leadership early, and deliver outcomes with a metrics-driven approach.
Design and evolve ML infrastructure for the full lifecycle across diverse content types and brands, including data pipelines, workflow orchestration, feature stores, batch training, and low-latency online serving; champion reliability, quality, and operational excellence.
Stay ahead of ML/AI/data engineering trends and champion adoption of new technologies and patterns to improve outcomes across the portfolio, including recommendation systems, LLMs, RAGs, object detection, and autogenerated content tagging.
Own production incident engagement by speaking to production incidents during weekly leadership meetings; drive appropriate engineering processes and foster a culture of reliability, observability, and continuous improvement.
Contribute to broader ML community efforts within Disney, including participation across Disney Entertainment & ESPN; influence engineering standards, cross-org programs, and shared best practices beyond the N&E team.
Connect platform investment to business and guest outcomes by understanding business objectives and problems across the portfolio; lead metrics-driven programs that tie ML improvements to measurable experience and business results.
Mentor and elevate senior engineers, building a culture of ownership, technical rigor, and continuous learning; act as a visible, collaborative leader focused on outcomes.

Requirements

Bachelor’s degree in computer science, Information Systems, Statistics, Math, or a comparable field (or equivalent work experience).
10+ years building and operating ML engineering systems in production environments, with experience owning large, complex problem spaces.
Deep expertise in data science, deep learning algorithms, and statistical methods applied to large-scale real-world engineering problems.
Proven architecture ownership across a significant platform or product domain, including authoring architecture documents, leading reviews, and driving implementation.
Demonstrated impact driving measurable improvements in ML platform capabilities, personalization quality, or recommendation system performance.
Backend microservices experience for large-scale distributed systems using REST.
Strong cloud expertise, preferably AWS (Step Functions, Lambda, Glue, SQS, SNS, Personalize).
Hands-on big data experience with Databricks, Spark, Kinesis, and Kafka.
Incident response leadership for high priority incidents and experience driving reliability programs across a team or platform.
Active cross-org participation in engineering communities, standards-setting, and architectural governance.
Metrics-driven technical leadership that connects engineering decisions to business outcomes.
Exceptional communication and influence with comfort presenting to and aligning senior leadership and cross-functional stakeholders.
Agile/Scrum experience with prioritization and stakeholder management.

Preferred Qualifications

Experience with agentic AI workflows and frameworks (e.g., LangGraph, AutoGen, CrewAI) applied to automate complex ML and data engineering tasks.
Familiarity with AI-assisted development tools such as Claude, Cursor, or GitHub Copilot.
Familiarity with prompt engineering, fine-tuning, and evaluation frameworks for large language models in production.
Experience with MLOps and modern model lifecycle management tools (e.g., MLflow, SageMaker, Vertex AI).

Technologies

REST
AWS: Step Functions, Lambda, Glue, SQS, SNS, Personalize
Databricks, Spark, Kinesis, Kafka
LLMs, RAGs
Object detection
LangGraph, AutoGen, CrewAI
Claude, Cursor
GitHub Copilot
MLflow, SageMaker, Vertex AI
Agile/Scrum

Location: Glendale, CA (onsite)

Compensation: USD 210,500 - 295,900 per year

View full job page →

Today

Senior Machine Learning Engineer

Disney Entertainment & Sports LLC Glendale, CA

Glendale, CA

Today

$144k - $193k/yr Full time Artificial Intelligence Automation AWS

Apply Now

Job Description

Join ESPN to build and run the distributed data and machine learning infrastructure behind real-time sports personalization and live experiences.

Responsibilities

Build and maintain high-throughput batch and streaming data pipelines for ML, analytics, and real-time decisioning
Implement data ingestion, enrichment, aggregation, and transformation using modern distributed data frameworks
Deliver pipelines that meet latency, reliability, and data quality needs for downstream ML and product teams
Develop and operate systems for real-time feature computation and delivery used by online ML services
Partner with applied ML and data science teams to enable production ML workflows through reliable data systems
Work with feature stores and event-driven architectures to keep offline and online data consistent
Improve data freshness, schema evolution, and backward compatibility for streaming environments
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
Collaborate with platform and SRE teams to improve availability, performance, and cost efficiency
Instrument data and ML infrastructure with metrics, logging, and alerting for production operations
Participate in on-call rotations and incident response for data and ML platforms
Diagnose and remediate data pipeline failures, performance regressions, and operational risks
Participate in design reviews, code reviews, and technical discussions
Follow established platform standards and contribute incremental improvements over time

Requirements

5+ years of industry experience building data-intensive or ML-adjacent systems in production
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 technologies such as Kafka, Kinesis, Spark, Flink, or equivalent
Proficiency in Python and working knowledge of Java, Scala, Go, or C++
Experience operating in cloud-native environments including AWS, containers, Kubernetes, and IaC tools
Familiarity with observability and operational best practices for production systems
Strong collaboration skills across engineering and data teams
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 including 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

Technologies

Kafka, Kinesis
Spark, Flink
Python
Java, Scala, Go, C++
AWS
Containers, Kubernetes, IaC tools
Feature stores

Education

Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field

Location and Salary

Location: Glendale, CA (onsite)
Compensation: USD 144,100 - 193,200 per year

View full job page →

2mo ago

Senior Data Engineer

Software Resources, Inc. Glendale, CA

Glendale, CA

2mo ago Closed

$90 - $93/hr Full time Senior Big Data Data Architecture

This position is no longer accepting applications (closed Sep 18, 2026).

Job Description

The Senior Data Engineer will support enterprise data engineering initiatives by designing, building, and optimizing scalable data solutions. The role includes owning and operating the Core Data platform, delivering both batch and streaming Spark pipelines, and applying governance across AWS, Kubernetes, and Airflow.

Role Overview

This position focuses on Core Data platform delivery and ongoing operations. The engineer will manage Databricks platform governance, develop pipelines with PySpark and related tooling, and ensure platform reliability through monitoring, cost awareness, automation, and troubleshooting across deployed services.

Key Responsibilities

Manage Databricks platform governance, including Unity Catalog, ACLs, lineage, and data discovery and privacy tooling
Design, write, test, and deploy batch and streaming data pipelines using PySpark, Scala, SQL, Python, and Kotlin
Meet with stakeholders to gather requirements and translate them into scalable data platform solutions
Use knowledge of Databricks platform and developer tooling to diagnose errors, audit platform activity, and automate updates across pipelines, objects, and integrations
Explain Spark architecture and pipeline behavior to stakeholders to identify root causes and recommend solutions
Provide solution architecture across AWS, Databricks, Kubernetes, and Airflow (MWAA), including cross-platform integrations
Build and maintain Kubernetes containers and containerized utilities that support deployed data platform services
Apply networking knowledge to troubleshoot connectivity and integration errors across platform components
Perform platform administration such as provisioning and removing access, assessing resource utilization, monitoring platform health and cost, and evaluating stakeholder requests
Collaborate with engineers, architects, and product managers to drive Core Data platform success and participate in agile or scrum ceremonies
Maintain documentation for platform changes, standards, and pipeline configurations to support data quality and governance
Develop and maintain scalable data engineering solutions, including building and enhancing data pipelines and workflows
Support cloud-based data platform initiatives and collaborate with engineering teams on system architecture and design
Implement infrastructure automation and deployment standards and troubleshoot and optimize data processing environments

Required Qualifications

Strong experience with Databricks
Advanced Python development skills
Experience with Terraform and Infrastructure as Code
5+ years of relevant data engineering experience
Ability to participate in architecture and system design discussions
Strong problem-solving and data platform implementation experience

Additional Qualifications

5+ years of data engineering experience developing and operating large-scale data pipelines
Deep hands-on experience with Databricks and Apache Spark (batch and streaming), including pipeline development in PySpark and/or Scala
Strong understanding of Spark architecture (executors, stages, partitioning, shuffle) with ability to explain performance tuning tradeoffs to technical and non-technical stakeholders
Proficiency with Databricks platform tooling (API, SDK, CLI) for automation, auditing, governance, and operational troubleshooting
Proficient in SQL with advanced performance tuning capabilities
Hands-on production experience with Airflow (MWAA) for orchestrating data pipelines
Experience managing Databricks governance, including ACLs, Unity Catalog, lineage, and access provisioning
Proficiency in Python and at least one additional language (Scala, Kotlin, or SQL-driven pipeline tooling)
Experience designing and optimizing scalable ETL/ELT pipelines integrating diverse structured and unstructured data sources
AWS-primary experience (compute, storage, networking, IAM); experience with other cloud providers is transferable
Proficiency with Docker and Kubernetes for building and maintaining containerized data platform services
Working knowledge of networking concepts to diagnose cross-platform integration and connectivity issues
Familiarity with Snowflake and comparable tooling relative to the Databricks ecosystem
Experience implementing CI/CD and DevOps practices using Git-based workflows
Experience implementing data quality checks, monitoring, and logging for pipeline reliability
Self-starting problem solver with strong analytical and communication skills and willingness to learn new tooling and trends
Familiar with Scrum and Agile methodologies
Bachelor’s Degree in Computer Science, Information Systems, or related field, or equivalent work experience; Master’s Degree is a plus

Preferred Qualifications

Experience with AWS
Cloud-based data platform experience
Data pipeline optimization and automation expertise

Technology Stack

Databricks, Apache Spark
Python, PySpark, Scala, SQL, Kotlin
Terraform, Infrastructure as Code
AWS, Kubernetes, Docker
Airflow (MWAA)
Unity Catalog
Git-based workflows, CI/CD

Location and Schedule

Glendale, CA (hybrid)
Duration: 12+ months

Compensation

Pay rate: USD 90 - 93 per hourly (DOE)

Benefits

Medical, dental, and vision coverage
401(k) with company match
Short-term disability
Life insurance with AD&D

View full job page →

2mo ago

Research Data Scientist - AI Trainer

DataAnnotation Glendale, CA 🇺🇸 Remote

Glendale, CA

2mo ago Closed

$50 - $100/hr Full time Ai Training Artificial Intelligence Data Analysis

This position is no longer accepting applications (closed Sep 8, 2026).

Job Description

Join DataAnnotation to help evaluate and improve AI systems that do real quantitative work. This fully remote role offers a flexible schedule, paid hourly projects, and direct impact on the next generation of AI models focused on statistical reasoning, forecasting, and data-driven analysis.

What you’ll do

Evaluate AI-generated quantitative outputs for technical accuracy and real-world validity, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights.
Design and solve quantitative problems used to train and benchmark AI systems across forecasting, experimental analysis, optimization, and statistical inference.
Produce clear technical explanations and deliver well-documented analytical code.
Provide actionable feedback that informs future AI model iterations for quantitative reasoning.

What you bring

2+ years of hands-on experience in a quantitative role or research environment, such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or an adjacent field.
Coding experience with the ability to write and review analytical code end-to-end.
Practical experience with statistical methods, predictive modeling, and experiment design, including examples like A/B testing, hypothesis testing, regression, classification, and time-series forecasting.
English fluency (native or bilingual level) with strong writing skills.
Bachelor’s degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar). A master’s or PhD is a plus.
Relevant credentials are a plus, such as Kaggle Competition ranking or AWS/GCP ML certifications (or equivalent demonstrated expertise).

Remote-friendly role details

Fully remote from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand (role not available outside these regions).
Flexible schedule: choose which projects you take on and when you work.
Competitive pay: projects are paid hourly, up to $60 USD/hour, with potential for higher-paying projects based on performance.
Payment method: paid via PayPal.
No-cost requirement: we will never ask for any money from you.

Technology

PayPal, AWS, GCP

Location: Glendale, CA (remote). Compensation: USD 50-100 per hourly.

View full job page →

2mo ago

Principal Data Scientist - Games

Disney Experiences Glendale, CA

Glendale, CA

2mo ago Closed

$156k - $209k/yr Full time Analytics Big Data Business Analytics

This position is no longer accepting applications (closed Sep 2, 2026).

Job Description

Disney Experiences is seeking a Principal Data Scientist to lead games data science and analytics for Disney Digital Entertainment in partnership with Epic Games.

Responsibilities

Act as the primary lead for games data science and analytics, serving as a central source of truth for games product performance, player behavior, and business insights
Partner with leadership and cross-functional teams to deliver actionable insights and strategic recommendations for critical business and product decisions
Create and present data-driven business and product performance analyses for senior leadership, translating complex data into clear narratives, executive decks, and strategy recommendations
Build and apply advanced analytical frameworks, statistical analyses, forecasting models, and behavioral segmentation approaches focused on engagement, retention, churn, monetization, lifecycle behavior, and content performance
Analyze gameplay telemetry, social engagement data, creator content trends, streaming interaction data, and cross-TWDC engagement signals to identify growth opportunities and improve user experiences across games and digital entertainment ecosystems
Work with cross-functional partners to define KPIs, measurement frameworks, instrumentation requirements, and scalable analytics solutions for product development and strategic planning
Lead deep-dive analyses on player progression systems, engagement funnels, franchise affinity, personalization opportunities, social behaviors, and monetization trends to inform data-driven product strategy
Collaborate with data engineering and platform teams to develop scalable analytics infrastructure, event pipelines, trusted datasets, and self-service reporting solutions
Serve as a subject matter expert in games analytics, measurement strategy, and product performance insights, and drive organizational best practices
Translate technical findings into clear recommendations for both technical and non-technical audiences, including senior leadership

Requirements

Bachelor’s degree in Data Science, Statistics, Mathematics, Economics, Computer Science, or a related quantitative field; advanced degree preferred
10+ years of experience in data science, advanced analytics, or quantitative product analytics within gaming, streaming, or entertainment industries
Proven experience applying statistical analysis, forecasting, experimentation frameworks, and behavioral analytics techniques to large-scale consumer and telemetry datasets
Strong proficiency in SQL and Python or R, plus hands-on experience with modern cloud data ecosystems and analytics platforms including Snowflake, Databricks, AWS, Tableau, and Looker
Experience with large-scale event-stream and gameplay telemetry datasets, including player behavior, audience engagement, content performance, progression systems, retention, and monetization analysis
Familiarity with audience segmentation, personalization strategies, recommendation systems, or content optimization frameworks supporting player engagement
Strong understanding of player or consumer lifecycle analytics, engagement measurement, retention analysis, segmentation, monetization optimization, and experimentation methodologies including A/B testing
Demonstrated ability to influence strategic decisions through data-driven storytelling, executive communication, and cross-functional collaboration in fast-paced, matrixed organizations

Technologies

SQL
Python
R
Snowflake
Databricks
AWS
Tableau
Looker
A/B testing

Location

Glendale, CA (onsite)

Compensation

Hiring range in Glendale, CA: $155,700 to $208,700 per year
Base pay offered may consider internal equity, candidate geographic region, job-related knowledge, skills, and experience, among other factors
Bonus and/or long-term incentive units may be provided, in addition to medical, financial, and/or other benefits depending on level and position offered

View full job page →

2mo ago

Data Scientist

LinQ Global Group Glendale, CA

Glendale, CA

2mo ago Closed

Full time Python A/B Testing Big Data

This position is no longer accepting applications (closed Aug 29, 2026).

Job Description

LinQ Global Group seeks a Data Scientist to design and analyze A/B tests and causal inference studies, partnering with cross-functional teams to generate insights and build scalable experimentation solutions using advanced statistical methods.

Responsibilities

Design and run A/B tests and geo-based experiments to validate ideas and measure impact.
Analyze data with statistical and causal-inference techniques to uncover actionable insights.
Create scalable analytics and experimentation platforms to support ongoing measurement.
Develop statistical models and machine learning solutions to inform business decisions.
Perform sample size planning, power analysis, and post-experiment evaluation to ensure reliable results.
Present findings and recommendations to business stakeholders and leadership.
Collaborate with cross-functional teams to address business problems through data-driven approaches.
Mentor team members and promote best practices in experimentation and data science.

Requirements

Bachelor's degree in Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field.
7+ years of experience in experimentation, causal inference, or data science.
Strong knowledge of A/B testing, experimental design, and statistical modeling.
Experience with causal inference methods such as Difference-in-Differences, Propensity Score Matching, and Instrumental Variables.
Proficiency in Python and/or R.
Experience with machine learning libraries such as scikit-learn or LightGBM.
Strong analytical, problem-solving, and communication skills.