Staff Machine Learning Engineer, Ads Prediction

Reddit is a community of communities. It’s built on shared interests, passion, and trust and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 73+ million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit redditinc.com.

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

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.

The base pay range for this position is:

$230,000—$322,000 USD

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