Reddit is hiring a
Remote Staff Software Engineer Machine Learning
\nReddit is continuing to grow our teams with the best talent. This role is completely remote friendly and will continue to be after the pandemic. \n\nWe’re evolving and continuing our mission to bring community, belonging, and empowerment to everyone in the world. Providing a delightful and relevant experience to our users applies to our Ads like all of our offerings, and we’re excited to build a product that is best-in-class for our users and advertisers. The year ahead is a busy one - join us! \n\nAds prediction team is the central team to handle machine learning needs in the ads delivery pipeline. Some examples projects that the team own:\n\n\nImprove our model through systematic model architecture engineering work including exploring different deep neural network architectures\n\nSystematic feature engineering work to build power features from Reddit’s data with aggregation, embedding, content understanding techniques\n\nDeveloping highly efficient retrieval ranking models with good balance between model performance and computation efficiency \n\n\n\n\nAs a Staff Machine Learning Engineer in the ads prediction team, you will research, formulate and execute on our mission to deliver the right ad to the right user under the right context with data and ML driven solutions. \n\nResponsibilities:\n\n\nBuilding industrial level models for critical ML tasks with advanced modeling techniques\n\nResearch, implement, test, and launch new model architectures including deep neural networks with advanced pooling and feature interaction architectures\n\nResearch, implement, test, and launch new model architectures to handle retrieval ranking tasks with a good balance between model performance and computation efficiency\n\nSystematic 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. \n\nBe a mentor and cross-functional advocate for the team\n\nContribute meaningfully to team strategy. We give everyone a seat at the table and encourage active participation in planning for the future.\n\n\n\n\nRequired Qualifications:\n\n\n* \n\n\n2+ years of experience with industry-level deep learning models\n\n5+ years of experience with mainstream ML frameworks (such as Tensorflow and Pytorch)\n\nEnd-to-end experience of training, evaluating, testing, and deploying industry-level models\n\nExperience of orchestrating complicated data generation pipelines on large-scale dataset\n\nExperience with Ads domain is a plus\n\nExperience with recommendation system is a plus\n\n\n\n\n\n\n\n\nBenefits:\n\nComprehensive Health Benefits\n\nRetirement Savings plan with matching contributions\n\nWorkspace benefits for your home office\n\nPersonal & Professional development funds\n\nFamily Planning Support\n\nFlexible Vacation & Reddit Global Days Off\n\n\n\n\n \n\n#LI-JS2 \n\n#Salary and compensation\n
No salary data published by company so we estimated salary based on similar jobs related to Engineer and Engineer jobs that are similar:\n\n
$60,000 — $110,000/year\n
\n\n#Benefits\n
💰 401(k)\n\n🌎 Distributed team\n\n⏰ Async\n\n🤓 Vision insurance\n\n🦷 Dental insurance\n\n🚑 Medical insurance\n\n🏖 Unlimited vacation\n\n🏖 Paid time off\n\n📆 4 day workweek\n\n💰 401k matching\n\n🏔 Company retreats\n\n🏬 Coworking budget\n\n📚 Learning budget\n\n💪 Free gym membership\n\n🧘 Mental wellness budget\n\n🖥 Home office budget\n\n🥧 Pay in crypto\n\n🥸 Pseudonymous\n\n💰 Profit sharing\n\n💰 Equity compensation\n\n⬜️ No whiteboard interview\n\n👀 No monitoring system\n\n🚫 No politics at work\n\n🎅 We hire old (and young)\n\n
\n\n#Location\nToronto, Ontario, Canada
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