DoorDash
Applied Machine Learning Engineer - Causal Inference Recommendation
DoorDash, Washington, District of Columbia, us, 20022
Applied Machine Learning Engineer - Causal Inference Recommendation
Qualifications
3+ years of industry experience developing advanced machine learning models with business impact, and shipping ML solutions to production Expertise in applied ML for Causal Inference and Recommendation Systems - both classical and deep learning based Additional familiarity with experimentation, computer vision, and LLMs M.S. or PhD in Statistics, Computer Science, Economics, Math, Operations Research, Physics, or other quantitative fields Ability to communicate technical details to nontechnical stakeholders Familiarity with Kotlin/Scala You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down The desire for impact with a growth-minded and collaborative mindset Responsibilities
Utilize our robust data and machine learning infrastructure to build recommendation systems and implement new AI solutions to expand restaurant selection and drive their growth Conceptualize, design, and evaluate A/ML solutions Demonstrate a strong command of production-level machine learning, collaborating with multi-disciplinary teams to set strategy and execute to grow our business Partner with engineering and product leaders to help shape the product roadmap leveraging AI/ML Own the modeling life cycle end-to-end including feature creation, model development and deployment, experimentation, monitoring and explainability, and model maintenance Benefits
Comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave, and more Location-specific base salary range: $140,100—$210,100 USD Compensation may vary based on skills, prior relevant experience, and specific work location Opportunities for equity grants included
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3+ years of industry experience developing advanced machine learning models with business impact, and shipping ML solutions to production Expertise in applied ML for Causal Inference and Recommendation Systems - both classical and deep learning based Additional familiarity with experimentation, computer vision, and LLMs M.S. or PhD in Statistics, Computer Science, Economics, Math, Operations Research, Physics, or other quantitative fields Ability to communicate technical details to nontechnical stakeholders Familiarity with Kotlin/Scala You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down The desire for impact with a growth-minded and collaborative mindset Responsibilities
Utilize our robust data and machine learning infrastructure to build recommendation systems and implement new AI solutions to expand restaurant selection and drive their growth Conceptualize, design, and evaluate A/ML solutions Demonstrate a strong command of production-level machine learning, collaborating with multi-disciplinary teams to set strategy and execute to grow our business Partner with engineering and product leaders to help shape the product roadmap leveraging AI/ML Own the modeling life cycle end-to-end including feature creation, model development and deployment, experimentation, monitoring and explainability, and model maintenance Benefits
Comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave, and more Location-specific base salary range: $140,100—$210,100 USD Compensation may vary based on skills, prior relevant experience, and specific work location Opportunities for equity grants included
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