Senior Machine Learning Engineer

Warner Bros. Discovery

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: San Francisco, CABellevue, WAAtlanta, GABurbank, CA
Salary: $159,180–$295,620 / yr
Posted: 23 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $220k

This role $227k

$143k most similar roles pay here $312k

This role pays more than 54% of similar roles. Most pay $184,625–$254,750 — the shaded band above. At the midpoint, this role pays about $227k versus about $220k for comparable roles.

Based on 240 similar postings.

Employer

About Warner Bros. Discovery

Warner Bros. Discovery is a global media and entertainment company operating a broad portfolio of iconic content and brands including Warner Bros. film studio, HBO, CNN, Discovery Channel, and Max streaming service. Industry: Media & Entertainment

Warner Bros. Discovery currently has 55 open roles on FindRole.

Listed pay typically runs $131,985–$245,115 across 38 roles with salary data.

Most-posted roles

View all roles at Warner Bros. Discovery

At a glance

TL;DR · Senior Machine Learning Engineer

Role Posting Log in to save

Senior Machine Learning Engineer The Senior Machine Learning Engineer joins the Data & Audience Platform team to build foundational AI/ML intelligence powering identity, audience, advertising, and personalization across various brands. You will own the end-to-end design and delivery of production ML systems, including data sourcing, feature engineering, model training, and deployment. Key responsibilities involve leading flagship projects like probabilistic identity resolution, lookalike modeling, and demand forecasting while establishing architectural patterns for the broader organization. You will work extensively with Databricks (PySpark, Delta, MLflow), Snowflake, and AWS SageMaker to build scalable pipelines. The role requires expertise in Python, SQL, and advanced techniques such as gradient boosting, neural ranking, and causal inference. You will also leverage agentic AI tools like LangChain and GitHub Copilot to automate workflows and optimize content discovery for a global streaming audience.

What does a Machine Learning Engineer earn in California?

Median $246394 from 172 postings across 28 companies.

See salary data

Skills

Databricks Python SQL Snowflake AWS SageMaker MLflow PySpark Delta Lake LangChain LangGraph XGBoost LightGBM S3 Lambda Unity Catalog Feature Stores RAG mLOps

What you'll do

Design and deliver end-to-end production ML systems including data sourcing, feature engineering, model training, and monitoring.
Own the technical direction for flagship products like identity resolution, lookalike modeling, and demand forecasting.
Develop scalable feature and inference pipelines using Databricks, PySpark, and Snowflake.
Build and maintain robust MLOps infrastructure including model versioning, drift detection, and automated retraining.
Implement advanced machine learning models such as gradient boosting, neural ranking, and two-tower retrieval.
Integrate agentic AI workflows and tools like LangChain or Databricks Genie to automate repetitive engineering tasks.
Mentor junior engineers and serve as a technical bridge for the global team across different time zones.
Translate business requirements from marketing and sales stakeholders into executable ML roadmaps and technical specifications.

What we're looking for

Must have 5–8 years of experience in ML engineering or applied data science (3+ years with a PhD).
Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related quantitative field.
Deep Python expertise and strong software engineering practices for building and deploying ML at scale.
Proficiency in Databricks (PySpark, Delta Lake, Workflows/DLT, MLflow, Unity Catalog) and Snowflake.
Experience with AWS ML services including SageMaker, S3, and Lambda.
Expertise in model evaluation, A/B testing, and statistical or causal inference techniques.
Demonstrated technical leadership in architectural decision-making, setting standards, and mentoring engineers.
Strong communication skills to translate business requirements into technical ML roadmaps for diverse stakeholders.