Engineering Manager, Consumer Inference, MLP

Netflix is the world's leading streaming entertainment service with 238M paid memberships in over 190 countries enjoying TV series, documentaries, and feature films across a wide variety of genres and languages. Machine Learning drives innovation across all product functions and decision-support needs. Building highly scalable and differentiated ML infrastructure is key to accelerating this innovation.

The Opportunity

We are looking for an experienced engineering leader to lead the Inference Platform team in the Machine Learning Platform organization. Our organization is chartered to maximize the business impact of all ML practitioners at Netflix and innovate on ML infrastructure to support key product functions like personalized recommendations, studio innovations, virtual productions, growth intelligence, and content demand modeling among others.

The Consumer Inference team is responsible for building products, libraries, and services that make it easy for researchers to take their offline ML ideas and easily deploy them to various production environments for consumer facing applications.  This team plays a critical role in transitioning ML algorithms into products where it can realize value for Netflix.  As a part of this role you will lead a team responsible for the following.

Consumer Scale Inference: Libraries and systems that enable flexible and high-performance processing of data and ML models for Netflix’s 238 million members across a diversity of modeling paradigms.

Model Lifecycle Management: Easy deployment, retirement, and configuration of ML models in online and offline environments.

GPU Inference: Increasingly complex ML models have introduced more opportunities to leverage hardware to meet cost-efficiency and performance needs.

ML Observability: Tools for self-service detection and remediation of ML quality issues in production settings.

To be successful in this role you will need the following skills:

• Vision: Understanding where the ML needs across a diverse set of use cases are today and will be in the future will allow you to lead your team by providing clear technical and business context.

• Partnership & Culture: Establishing positive partnerships with both business and technical leaders across Netflix will be critical. We want you to regularly demonstrate the Netflix culture values like selflessness, curiosity, context over control, and freedom & responsibility in all your engagements with colleagues.

• Judgment: Netflix teams tend to be leaner compared to our peer companies, so you will rely on your judgment to prioritize projects, working closely with your partners - the personalization research leaders.

• Technical acumen: We expect leaders at Netflix to be well-versed in their technical domain and be a user of the products we are building, so they can provide guidance for the team when necessary. You should have some prior experience running ML infrastructure systems at scale.

• Team Building: Building and growing a team of outstanding engineers will be your primary responsibility. You will strive to make the team as excellent as it can be, hiring and retaining the best, and providing meaningful timely feedback to those who need it.

Minimum Job Qualifications

• Prior experience leading a team responsible for ML infrastructure

• Strong product sense – you take pride in building well-designed products that users love.

• Outstanding people skills with high emotional intelligence

• Excellent at communicating context, giving and receiving feedback, fostering new ideas, and empowering others without micromanagement

• Willing to take action, without being stubborn - the ability to recognize your own mistakes

• Your team and partners see your humility all the time and diverse high-caliber talent wants to work with you

Preferred Qualifications

• 10+ years of total experience including 3+ years of engineering management

• Prior experience working on ML inference or model lifecycle management, ideally at large scale

• Experience with deploying Tensorflow, PyTorch, XGBoost in production.

• Exposure to modern ML serving systems and frameworks, such as Ray Serve, NVIDIA Triton, ONNX runtime.

At Netflix, we carefully consider a wide range of compensation factors to determine your personal top of market. We rely on market indicators to determine compensation and consider your specific job, skills, and experience to get it right. These considerations can cause your compensation to vary and will also be dependent on your location.

The overall market range for roles in this area of Netflix is typically $180,000 - $900,000.

This market range is based on total compensation (vs. only base salary), which is in line with our compensation philosophy. Netflix is a unique culture and environment. Learn more here.