Staff Machine Learning Engineer

Description

The company's Growth Content Science team builds the machine learning that decides which content reaches people on growth surfaces, with notifications as the main focus.

The team owns the real-time production stack: candidate retrieval, first and second stage ranking, utility and blending, content quality, and copy generation and ranking.

As Staff Machine Learning Engineer, you build the most complex models and systems on this stack, set the bar for code quality and ML infrastructure, and lead the technical direction for notification content ranking.

Responsibilities

• You lead several technical sub-areas within Growth Content Science.
• You own the health of the ML ecosystem, including training pipelines, feature and serving infrastructure, evaluation and monitoring, and A/B test setup.
• You drive model gains through a loop of A/B testing, from ideas through to production launches.
• You set technical direction and roadmaps with other engineering leaders.
• You lead large cross-functional initiatives alongside colleagues and stakeholders.
• You raise the technical bar through mentoring, reviews, evaluations, documentation and standards.
• You work autonomously in complex, changing environments and keep work moving.
• You find and apply relevant recent research, or help shape new research directions.
• You keep improving your work with current AI tools.

Requirements

• A graduate degree in computer science or a related field.
• At least 8 years in machine learning engineering, including building and shipping models for a large, real-time, user-facing recommender system.
• Deep knowledge of recommender modeling, including sequence modeling of user behavior with transformers, DNN rankers, and generative and LLM-native recommenders.
• Experience building and running high-QPS, latency-sensitive ML systems, training and serving included, and owning the trade-offs between cost, latency and reliability.
• Industry-grade software engineering skills that others use as a model, and thorough code and design reviews that improve a team's output.
• An understanding that content personalization and content quality are one problem, and why cold-start and low-signal users break standard rankers.
• Experience leading technical work across teams, making timely calls on hard trade-offs and declining requests when user experience or system health is at risk.
• Experience mentoring junior engineers.
• Excellent written and verbal communication, especially across functions.
• Demonstrated use of AI to speed up and improve applied research.
• A strong record of publications and patents.

Conditions

This role is open only to applicants based in the US.

You come into the office for in-person collaboration 1-2 times per quarter, and you can live anywhere in the country.

The position is not eligible for relocation assistance.

The base salary range is $189,308 - $389,753 USD. Final pay depends on factors such as location, travel, relevant prior experience, and specific skills and expertise.

Interviews assess your foundational skills and how you work with AI, so you should be able to explain your approach and reasoning.

The company aims for a workplace that is equitable, inclusive and welcoming to everyone.

Benefits

• The position is eligible for equity.
• Details on culture and benefits for this role are available from the company.