Engineering Manager - Machine Learning

About Momentive.ai

Momentive (NASDAQ: MNTV), formerly SurveyMonkey, is a leader in agile experience management. They deliver powerful, purpose-built solutions that combine humanity and technology to redefine AI. Momentive products, including GetFeedback, SurveyMonkey, and their brand and market insights solutions, empower decision-makers at 345,000 organizations worldwide to shape exceptional experiences. More than 20 million active users rely on Momentive to fuel market insights, brand insights, employee experience, customer experience, and product experience. The company's vision is to raise the bar for human experiences by amplifying individual voices.

More about our Machine Learning Platform Team

The Machine Learning Platform (MLP) team owns the complete ML operations pipeline and is responsible for building a machine learning platform that accelerates the efficient adoption of machine learning across all Momentive portfolio products. The goal is to build applications and tools that enable the scalability of ML along all points of the lifecycle of an AI project, from feature discovery to model training, from model deployment to post-production monitoring and evaluation. Today, the ML platform is powering millions of predictions and is looking to scale up to billions. The MLP consists of three teams: ML Experience, ML data platform, and ML serving and tooling. This role is to manage the ML serving and tooling team.

What we're looking for

You are technically hands-on with rich experience in real-time production ML inference systems, possessing the right blend of data, system architecture, and cloud-native solutions experience. As part of the ML Platform team, you will lead the team that owns the serving components and data science workflow tools. The team is responsible for architecting, implementing, maintaining, and scaling the ML inference pipeline for both real-time and batch processing. You will partner with Infrastructure, Data Science, Legal, Security, Application Engineering, and Product teams to extend and scale up the platform. You are well versed and passionate about productionizing machine learning solutions and are excellent at partnering with the engineering community by encouraging win-win relationships.

Key Responsibilities

• Provide architectural oversight for the ML serving and tooling team, continuing to evolve and scale the machine learning inference pipeline.
• Be responsible for the overall ML serving pipeline, owning the SLA requirements for both real-time and batch inferences.
• Collaborate seamlessly with the ML experience and ML data platform team to scale up the machine learning platform to the next level.
• Be a close partner with the product data science team to provide tooling for data science workflow, supporting model training, model deployment, etc.
• Be an architectural voice and partner with infrastructure and applications engineering teams to ensure machine learning requirements are understood and incorporated in architecture decisions.
• Manage a team of 5 engineers and set the strategy for scaling to more if needed.
• Ensure the team has the tools and capabilities to provide operational excellence on the ML platform and throughout the ML project lifecycle.
• Promote excellence in execution by streamlining agile processes, providing clarity in project definition, and making smart trade-offs between tech debt accumulation and pay off.

Required Qualifications

• 8+ years of hands-on software engineering experience.
• 2+ years of management experience.
• Deep technical experience with building production machine learning platforms at scale in the cloud, especially on the serving side.
• Rich experience with solving the unique challenges in different stages of the machine learning project lifecycle, specifically on the serving phase.
• Great understanding of the lifecycle of ML-enabled products.
• Well-versed in large-scale streaming and data processing systems and know when to use out-of-the-box solutions versus building custom ones.
• A strong systems background in one of the Cloud environments.
• Good understanding of applied machine learning techniques, including natural language processing, classification, spam detection, personalization/ranking, etc.
• Demonstrated platform ownership and customer service through uptime, responsiveness, and positive action.
• A track record for making it happen - rapid and clean execution.
• Understanding of natural language processing and Amazon Web Services (AWS) may be beneficial.