Software Engineer
Netflix
full-time
Posted on:
Location Type: Remote
Location: Remote • Washington • 🇺🇸 United States
Visit company websiteSalary
💰 $100,000 - $720,000 per year
Job Level
Mid-LevelSenior
Tech Stack
CloudDistributed SystemsKubernetes
About the role
- Design and build the platform that powers large-scale machine learning model training, fine-tuning, model transformation and evaluations workflows and use cases from the entire company.
- Co-design and optimize the systems and models to scale up and increase the cost-effectiveness of machine learning model training.
- Design easy-to-use APIs and interfaces for experienced ML practitioners, as well as non-experts to easy access the training platform.
Requirements
- Experience in ML engineering on production systems dealing with training or inference of deep learning models.
- Proven track record of building and operating large-scale infrastructure for machine learning use cases.
- Experience with cloud computing providers, preferably AWS.
- Comfortable with ambiguity and working across multiple layers of the tech stack to execute on both 0-to-1 and 1-to-100 projects.
- Adopt and promote best practices in operations, including observability, logging, reporting, and on-call processes to ensure engineering excellence.
- Excellent written and verbal communication skills.
- Comfortable working in a team with peers and partners distributed across (US) geographies & time zones.
Benefits
- Health Plans
- Mental Health support
- 401(k) Retirement Plan with employer match
- Stock Option Program
- Disability Programs
- Health Savings and Flexible Spending Accounts
- Family-forming benefits
- Life and Serious Injury Benefits
- Paid leave of absence programs
- Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off.
- Full-time salaried employees are immediately entitled to flexible time off.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learning engineeringdeep learning modelslarge-scale infrastructureAPI designmodel trainingmodel fine-tuningmodel transformationmodel evaluationcloud computingcost-effectiveness
Soft skills
communication skillsteam collaborationadaptabilityproblem-solvingoperational excellence