Staff Machine Learning Engineer, Ads Prediction

As a company, Reddit primarily generates revenue through advertising, and we're working towards building a massive business to fund our mission. We distinguish ourselves from other digital ad platforms by attracting advertisers who want to connect with a specific target audience because of our passionate and engaged communities.

Ads prediction team is responsible of predicting ads engagement rates used in auctions to maximize ad engagements and marketplace efficiency. This team owns a critical piece in the ads delivery pipeline and machine learning infrastructure. Project highlights:

• Model architecture engineering via exploring different state-of-the-art model architectures such as Multi-task learning, Attention Layer
• Systematic feature engineering to build power features from Reddit’s data with aggregation, embedding, sub-models, content understanding techniques, etc.
• Build efficient ML infrastructure and tools such as auto ML flows and batch feature engineering framework, to accelerate ML dev cycle

As a Staff machine learning engineer in the Ads prediction team, you will serve as a visionary in researching, formulating, and executing projects. You will actively participate in the end-to-end implementation process, and collaborate with cross-functional teams to ensure successful product delivery. You will be responsible for the quality and technical approach within the team; partner with other leads in direction setting, planning, and overseeing eng designs and executions; establish and contribute to the group’s culture and processes.

Your Responsibilities:

• Building industrial-level models for critical ML tasks with advanced modeling techniques
• Research, implement, test, and launch new model architectures including deep neural networks with advanced pooling and feature interaction architectures
• Systematic feature engineering works to convert all kinds of raw data in Reddit (dense & sparse, behavior & content, etc) into features with various FE technologies such as aggregation, embedding, sub-models, etc.
• Be a mentor and cross-functional advocate for the team
• Drive the formulation and execution of team strategy.

Who You Might Be:

• Tracking records of consistently driving KPI wins through systematic works around model architecture and feature engineering
• 5+ years of experience with industry-level deep learning models
• 5+ years of experience with mainstream ML frameworks (such as Tensorflow and Pytorch)
• 6+ years of end-to-end experience of training, evaluating, testing, and deploying industry-level models
• 6+ years of experience of orchestrating complicated data generation pipelines on large-scale dataset
• Experience with Ads domain is a plus
• Experience with recommendation system is a plus

Benefits:

• Comprehensive Healthcare Benefits
• 401k Matching
• Workspace benefits for your home office
• Personal & Professional development funds
• Family Planning Support
• Flexible Vacation (please use them!) & Reddit Global Wellness Days
• 4+ months paid Parental Leave
• Paid Volunteer time off

Pay Transparency:

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.

To provide greater transparency to candidates, we share base pay ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

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