Machine Learning Engineer 5 - Content and Studio

Netflix

10/15/2025

remote

Full-time

Salary: $150,000 - $750,000 a year

Job Description

Netflix is looking for an experienced machine learning engineer to develop, optimize, and deploy scalable ML solutions that power content decisions at Netflix.

Requirements

Advanced degree (MS or PhD) in Computer Science, Electrical Engineering, or a related technical field with a focus on machine learning or artificial intelligence.
At least 5 years of relevant industry experience designing and implementing ML solutions.
Proficient in Python and have experience with ML/DL frameworks such as PyTorch, MetaFlow, or Jax.
Strong foundation in machine learning, including supervised and unsupervised learning, and deep learning architectures.
Hands-on experience evaluating and monitoring machine learning systems in production.
Strong understanding of feature engineering, data pipelines, and model lifecycle management for large-scale data processing problems using tools like Spark.

Responsibilities

Own and innovate upon ML models that predict how members engage with content slate and future launching titles.
Design, build, and deploy robust ML systems which scale to handle Netflix-sized data.
Automate ML workflows for training, tuning, and deployment.
Optimize model performance and inference efficiency.
Improve ML observability, model evaluations, model monitoring, and debugging tools.
Collaborate with scientists, data engineers, and infrastructure teams to define project roadmaps.

Benefits

Employees at Netflix are often offered flexible, people-first benefits—unlimited time away, generous parental leave, global family-forming support, mental-health programs (mindfulness, free counseling/coaching), and health coverage tailored by country. Financially, Netflix pays at personal top-of-market and lets employees choose their mix of cash vs. fully-vested 10-year stock options, alongside donation and volunteer matching. Convenience perks can include trust-based travel/expense policies, relocation support, and “Work, Not Drive” rideshare flexibility.