Software Engineer, Training Platform Team

Internexxus; United States; Remote; USD 100,000 - 720,000; Full time

Job summary

An innovative company is seeking a talented Software Engineer to join its Training Platform Team focused on Machine Learning. This role involves designing and building scalable platforms for ML model training, optimizing infrastructure for cost-effectiveness, and creating user-friendly APIs. The ideal candidate will have experience with deep learning models, cloud computing, and a collaborative spirit to work across distributed teams. Join this forward-thinking organization to make a significant impact in the world of machine learning and contribute to cutting-edge projects that enhance user experiences globally.

Benefits

Stock Options

Comprehensive Benefits

Flexible Work Hours

Remote Work Opportunities

Qualifications

• Experience in ML engineering on production systems.
• Proven track record of building large-scale ML infrastructure.

Responsibilities

• Design and build a platform for large-scale ML model training.
• Optimize systems for cost-effective ML model training.

Skills

Machine Learning Engineering

Deep Learning Models

Cloud Computing (AWS)

API Design

Collaboration

Communication Skills

Tools

AWS

SageMaker

Databricks

OpenAI

Job description

Software Engineer, Training Platform Team

Netflix is a leading global entertainment service with 283 million paid memberships in over 190 countries. Members enjoy TV series, films, and games across a wide variety of genres and languages. The company utilizes Machine Learning/Artificial Intelligence extensively, powering innovations from personalized recommendations to optimizing revenue-focused initiatives. Building scalable and differentiated ML infrastructure is crucial to accelerating this innovation.

Job Description: Software Engineer, Training Platform Team (Machine Learning Platform)

Netflix’s Machine Learning Platform (MLP) org seeks a driven Software Engineer to join its Training Platform team. MLP aims to maximize the business impact of all ML use cases through reliable and flexible tooling and infrastructure. This supports key product functions including personalized recommendations, studio algorithms, virtual productions, growth intelligence, and content demand modeling.

Responsibilities:

1. Design and build: The platform powering large-scale machine learning model training, fine-tuning, model transformation, and evaluation workflows for the entire company.
2. Co-design and optimize: Systems and models to scale up and increase the cost-effectiveness of machine learning model training.
3. Design user-friendly APIs and interfaces: For both experienced ML practitioners and non-experts to easily access the training platform.

Minimum Qualifications:

1. Experience in ML engineering on production systems dealing with training or inference of deep learning models.
2. Proven track record of building and operating large-scale infrastructure for machine learning use cases.
3. Experience with cloud computing providers, preferably AWS.
4. Ability to work effectively with ambiguity and across multiple layers of the tech stack.
5. Adoption and promotion of best practices in operations (observability, logging, reporting, on-call processes).
6. Excellent written and verbal communication skills.
7. Ability to work collaboratively in a distributed team across US geographies and time zones.

Preferred Qualifications:

1. Understanding of modern ML model development workflows and experience partnering with ML modeling engineers.
2. Familiarity with cloud-based AI/ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI).
3. Experience with large-scale distributed training and parallelism techniques (FSDP, tensor/pipeline parallelism).
4. Expertise in Generative AI (training, fine-tuning, and distilling foundation models).

Compensation: Netflix offers a competitive salary ($100,000 – $720,000) with a structure consisting solely of an annual salary and stock options (employee choice of allocation). Comprehensive benefits are also provided.