Key skills
Machine Learning EngineeringLarge-scale InfrastructureCloud Computing (AWS)Distributed Training TechniquesGenerative AI ExpertiseAPI DesignCommunication SkillsTeam CollaborationAIMachine LearningMLDeep LearningGenerative AIOpenAIDatabricksAWSSageMakerBedrockCommunication
About this role
Netflix is a leading entertainment company focused on pushing the boundaries of storytelling and technology. They are seeking a driven Software Engineer to join their Training Platform team under the Machine Learning Platform (MLP) org, which aims to maximize the business impact of machine learning use cases through reliable ML tooling and infrastructure.
Responsibilities:
- 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
- Understand modern and real-world Machine Learning model development workflows and experience partnering closely with ML modeling engineers
- Familiarity with cloud-based AI/ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI, etc.)
- Experience with large-scale distributed training and different parallelism techniques for scaling up training, such as FSDP and tensor/pipeline parallelism
- Expertise in the area of Generative AI, specifically when it comes to training foundation models, fine tuning them, and distilling them to smaller models