Staff machine learning - job 2 of 2

About The TeamScroll down the page to see all associated job requirements, and any responsibilities successful candidates can expect.Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop the AI / ML that powers DoorDash's growing Drive Business.About The RoleWe’re looking for a passionate Applied Machine Learning expert to join our team. In this role, you will utilize our robust data and machine learning infrastructure to implement top-notch AI / ML solutions to build a seamless consumer voice ordering experience, and improve delivery quality for merchants, dashers, and consumers for the Drive Business.As a Staff Machine Learning Scientist, you’ll be conceptualizing, designing, evaluating, and implementing new AI solutions to DoorDash, building and fine-tuning Large Language Models.You will be expected to demonstrate a strong command of production-level machine learning, a passion for solving end-user problems, leadership skills to collaborate well with multi-disciplinary teams, and execution focused to prioritize effectively in a dynamic environment.You will be reporting into the Machine Learning Engineering Manager. This will be a hybrid position in San Francisco, Sunnyvale, Los Angeles, Seattle, or New York.You’re Excited About This Opportunity Because You Will• Develop production machine learning solutions to build a world-class voice ordering AI solution.• Bring top-notch Large Language Model solutions to DoorDash.• Partner with engineering and product leaders to help shape the product roadmap leveraging AI.• Lead cross-functional pods to generate collective impact.We’re Excited About You Because You Have• 7+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production.• 1+ years in a tech lead capacity.• Deep expertise in large language models, deep learning, and fine-tuning.• Strong machine learning background in Python; experience with Spark, PyTorch or TensorFlow preferred.• You must be located near one of our engineering hubs which includes : San Francisco, Sunnyvale, Los Angeles, Seattle, and New York.• M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field.• Familiarity with causal inference and experimentation preferred.About DoorDashAt DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users from Dashers to merchant partners to consumers.We are a technology and logistics company that started with door-to-door delivery, and we are looking for team members who can help us go from a company that is known for delivering food to a company that people turn to for any and all goods.DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers.We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.Our Commitment to Diversity and InclusionWe’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives.We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.Statement of Non-Discrimination : In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on : race, color, ancestry, national origin, religion, age, gender, marital / domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status.Above and beyond discrimination and harassment based on protected categories, we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office. Whether blatant or hidden, barriers to success have no place at DoorDash.We value a diverse workforce people who identify as women, non-binary or gender non-conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently-abled, caretakers and parents, and veterans are strongly encouraged to apply.Thank you to the Level Playing Field Institute for this statement of non-discrimination.Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.If you need any accommodations, please inform your recruiting contact upon initial connection.CompensationThe location-specific base salary range for this position is listed below. Compensation in other geographies may vary.For roles that are available to be filled remotely, base salary is localized according to employee work location. Please discuss your intended work location with your recruiter for more information.DoorDash cares about you and your overall well-being, and that’s why we offer a comprehensive benefits package, for full-time employees, that includes healthcare benefits, a 401(k) plan including an employer match, short-term and long-term disability coverage, basic life insurance, wellbeing benefits, paid time off, paid parental leave, and several paid holidays, among others.In addition to base salary, the compensation package for this role also includes opportunities for equity grants.California Pay Range :$176,000 $238,000 USDNew York Pay Range :$176,000 $238,000 USDWashington Pay Range :$176,000 $238,000 USDJ-18808-LjbffrLast updated : 2024-08-22