Machine Learning Intern (PhD) - Summer 2027
for section headings. Use - for any list of duties/requirements/benefits. Use
for prose.
We need to preserve all substantive information. Must not invent or summarize away detail. Must not include filler like "commensurate with experience". We have specifics.
We need to decide sections: About The Team, About The Role, Responsibilities? The "You’re Excited About This Opportunity Because You Will…" could be Responsibilities or maybe "What you’ll do". Could treat as Responsibilities. Also "We’re excited about you because you…" could be Requirements/Qualifications. Could combine into Requirements.
We also have "Applications for this position are accepted on an ongoing basis" - that's application instruction, should be removed.
We have "Compensation" heading in original; we can make Pay section.
We have benefits details.
We have schedule: 12-week summer internship program.
We have location offices.
We need to ensure we don't create sections with filler. All sections have real content.
Let's extract content.
About The Team: "DoorDash is building the world’s most reliable on-demand logistics engine for delivery! We’re looking for machine learning engineer interns to join our fast-growing engineering team to help us develop a 24x7 global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers."
That's the team description.
About The Role: "As a Machine Learning Engineer intern at Doordash, you’ll work on tackling new challenges in machine learning and artificial intelligence. You’ll conduct research that can be applied across Doordash engineering teams and engage in external collaborations and mentoring, while also performing research in any of the following areas: Auction, Game theory, Recommender systems, Ranking, AdTech, Computer Vision, Causal Inference, and Big data analytics."
That's role description.
Responsibilities (You’re Excited About This Opportunity Because You Will…): list:
- Use cutting-edge research in ML/AI, NLP, RecSys, Ranking, Computer Vision, Causal Inference, Ad Tech, Graph analysis to solve real-world problems across discovery, ads, forecasting, fulfillment and search experiences at Doordash.
- Contribute and execute on research ideas that can be applied and used to improve product experience at Doordash.
- Collect, analyze, and synthesize findings from data and use these insights to build relevant ML models.
- Write clean, efficient, and sustainable code.
We could put as
.
Requirements/Qualifications (We’re excited about you because you…): list:
- Are working towards a PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field and are graduating between Fall 2027 & Summer 2028
- Have a mastery of at least one systems languages (Java, C++, Python, Kotlin, GoLang) or one ML framework (Tensorflow, Pytorch, MLFlow)
- Have experience in research and in solving analytical problems
- Are a strong communicator and team player.
- Have a passion for applied ML and the Doordash product
- Ideally have worked on publications in machine learning, AI, data science, data analytics, statistics, or related technical fields
We could put as .
Pay: Compensation details:
- Base salary ranges: I4 $130,600—$192,000 USD; I5 $159,800—$235,000 USD; I6 $193,800—$285,000 USD.
- Base salary localized according to work location.
- In addition to base salary, compensation includes opportunities for equity grants.
- Talk to recruiter for more information.
We should keep the ranges as given.
Benefits: list:
- 401(k) plan with employer matching
- 16 weeks of paid parental leave
- Wellness benefits
- Commuter benefits match
- Paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act)
- Medical, dental, and vision benefits
- 11 paid holidays
- Disability and basic life insurance
- Family-forming assistance
- Mental health program
- PTO details: For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year. For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).
We could include that as part of benefits.
Schedule: 12-week summer internship program in San Francisco, Sunnyvale, New York, or Seattle offices.
We could also mention location.
We need to avoid duplication. Ensure we don't include boilerplate like "Applications for this position are accepted on an ongoing basis". Also remove "Compensation" heading? We'll make Pay section.
We need to output only HTML fragment.
Let's craft:
About the Team
DoorDash is building the world’s most reliable on-demand logistics engine for delivery! We’re looking for machine learning engineer interns to join our fast-growing engineering team to help us develop a 24x7 global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers.
About the Role
As a Machine Learning Engineer intern at Doordash, you’ll work on tackling new challenges in machine learning and artificial intelligence. You’ll conduct research that can be applied across Doordash engineering teams and engage in external collaborations and mentoring, while also performing research in any of the following areas: Auction, Game theory, Recommender systems, Ranking, AdTech, Computer Vision, Causal Inference, and Big data analytics.
Responsibilities
- Use cutting-edge research in ML/AI, NLP, RecSys, Ranking, Computer Vision, Causal Inference, Ad Tech, Graph analysis to solve real-world problems across discovery, ads, forecasting, fulfillment and search experiences at Doordash.
- Contribute and execute on research ideas that can be applied and used to improve product experience at Doordash.
- Collect, analyze, and synthesize findings from data and use these insights to build relevant ML models.
- Write clean, efficient, and sustainable code.
Qualifications
- Working towards a PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field and graduating between Fall 2027 & Summer 2028.
- Mastery of at least one systems language (Java, C++, Python, Kotlin, GoLang) or one ML framework (Tensorflow, Pytorch, MLFlow).
- Experience in research and solving analytical problems.
- Strong communicator and team player.
- Passion for applied ML and the Doordash product.
- Ideally have worked on publications in machine learning, AI, data science, data analytics, statistics, or related technical fields.
Pay
Base salary ranges (localized to work location):
- I4: $130,600—$192,000 USD
- I5: $159,800—$235,000 USD
- I6: $193,800—$285,000 USD
In addition to base salary, the compensation package includes opportunities for equity grants. Talk to your recruiter for more information.
Benefits
- 401(k) plan with employer matching
- 16 weeks of paid parental leave
- Wellness benefits
- Commuter benefits match
- Paid time off and paid sick leave in compliance with applicable laws (e.g., Colorado Healthy Families and Workplaces Act)
- Medical, dental, and vision benefits
- 11 paid holidays
- Disability and basic life insurance
- Family-forming assistance
- Mental health program
- Paid time off details: flexible paid time off/vacation plus 80 hours of paid sick time per year for salaried roles; for hourly roles, vacation accrued at about 1 hour for every 25.97 hours worked (≈6.7 hours/month at 40 h/wk, ≈3.4 hours/month at 20 h/wk) and paid sick time accrued at 1 hour for every 30 hours worked (≈5.8 hours/month at 40 h/wk, ≈2.9 hours/month at 20 h/wk)
Schedule
12‑week summer internship program located in our San Francisco, Sunnyvale, New York, or Seattle offices.
Check for any leftover boilerplate: We removed diversity statement, EEO, scam warnings, application instructions, etc. Also removed "About DoorDash" mission statement etc. That is considered boilerplate? The "About DoorDash" paragraph is generic company description; could be considered boilerplate but it's substantive about mission. However the instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The "About DoorDash" mission paragraph is not explicitly listed as boilerplate but could be considered generic. However it's not duplicated. It might be okay to keep? The instruction: "Prefer a few substantive sections over many thin ones." The About DoorDash paragraph is not about the role or team; it's generic company description. Might be considered boilerplate but not explicitly prohibited. Safer to omit? The instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. It does not say to remove generic company description. So we could keep it as part of About the Team
for prose. We need to preserve all substantive information. Must not invent or summarize away detail. Must not include filler like "commensurate with experience". We have specifics. We need to decide sections: About The Team, About The Role, Responsibilities? The "You’re Excited About This Opportunity Because You Will…" could be Responsibilities or maybe "What you’ll do". Could treat as Responsibilities. Also "We’re excited about you because you…" could be Requirements/Qualifications. Could combine into Requirements. We also have "Applications for this position are accepted on an ongoing basis" - that's application instruction, should be removed. We have "Compensation" heading in original; we can make Pay section. We have benefits details. We have schedule: 12-week summer internship program. We have location offices. We need to ensure we don't create sections with filler. All sections have real content. Let's extract content. About The Team: "DoorDash is building the world’s most reliable on-demand logistics engine for delivery! We’re looking for machine learning engineer interns to join our fast-growing engineering team to help us develop a 24x7 global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers." That's the team description. About The Role: "As a Machine Learning Engineer intern at Doordash, you’ll work on tackling new challenges in machine learning and artificial intelligence. You’ll conduct research that can be applied across Doordash engineering teams and engage in external collaborations and mentoring, while also performing research in any of the following areas: Auction, Game theory, Recommender systems, Ranking, AdTech, Computer Vision, Causal Inference, and Big data analytics." That's role description. Responsibilities (You’re Excited About This Opportunity Because You Will…): list: - Use cutting-edge research in ML/AI, NLP, RecSys, Ranking, Computer Vision, Causal Inference, Ad Tech, Graph analysis to solve real-world problems across discovery, ads, forecasting, fulfillment and search experiences at Doordash. - Contribute and execute on research ideas that can be applied and used to improve product experience at Doordash. - Collect, analyze, and synthesize findings from data and use these insights to build relevant ML models. - Write clean, efficient, and sustainable code. We could put as
- .
Requirements/Qualifications (We’re excited about you because you…): list:
- Are working towards a PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field and are graduating between Fall 2027 & Summer 2028
- Have a mastery of at least one systems languages (Java, C++, Python, Kotlin, GoLang) or one ML framework (Tensorflow, Pytorch, MLFlow)
- Have experience in research and in solving analytical problems
- Are a strong communicator and team player.
- Have a passion for applied ML and the Doordash product
- Ideally have worked on publications in machine learning, AI, data science, data analytics, statistics, or related technical fields
We could put as
- Use cutting-edge research in ML/AI, NLP, RecSys, Ranking, Computer Vision, Causal Inference, Ad Tech, Graph analysis to solve real-world problems across discovery, ads, forecasting, fulfillment and search experiences at Doordash.
- Contribute and execute on research ideas that can be applied and used to improve product experience at Doordash.
- Collect, analyze, and synthesize findings from data and use these insights to build relevant ML models.
- Write clean, efficient, and sustainable code.
- Working towards a PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field and graduating between Fall 2027 & Summer 2028.
- Mastery of at least one systems language (Java, C++, Python, Kotlin, GoLang) or one ML framework (Tensorflow, Pytorch, MLFlow).
- Experience in research and solving analytical problems.
- Strong communicator and team player.
- Passion for applied ML and the Doordash product.
- Ideally have worked on publications in machine learning, AI, data science, data analytics, statistics, or related technical fields.
- I4: $130,600—$192,000 USD
- I5: $159,800—$235,000 USD
- I6: $193,800—$285,000 USD
- 401(k) plan with employer matching
- 16 weeks of paid parental leave
- Wellness benefits
- Commuter benefits match
- Paid time off and paid sick leave in compliance with applicable laws (e.g., Colorado Healthy Families and Workplaces Act)
- Medical, dental, and vision benefits
- 11 paid holidays
- Disability and basic life insurance
- Family-forming assistance
- Mental health program
- Paid time off details: flexible paid time off/vacation plus 80 hours of paid sick time per year for salaried roles; for hourly roles, vacation accrued at about 1 hour for every 25.97 hours worked (≈6.7 hours/month at 40 h/wk, ≈3.4 hours/month at 20 h/wk) and paid sick time accrued at 1 hour for every 30 hours worked (≈5.8 hours/month at 40 h/wk, ≈2.9 hours/month at 20 h/wk)
- .
Pay: Compensation details:
- Base salary ranges: I4 $130,600—$192,000 USD; I5 $159,800—$235,000 USD; I6 $193,800—$285,000 USD.
- Base salary localized according to work location.
- In addition to base salary, compensation includes opportunities for equity grants.
- Talk to recruiter for more information.
We should keep the ranges as given.
Benefits: list:
- 401(k) plan with employer matching
- 16 weeks of paid parental leave
- Wellness benefits
- Commuter benefits match
- Paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act)
- Medical, dental, and vision benefits
- 11 paid holidays
- Disability and basic life insurance
- Family-forming assistance
- Mental health program
- PTO details: For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year. For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).
We could include that as part of benefits.
Schedule: 12-week summer internship program in San Francisco, Sunnyvale, New York, or Seattle offices.
We could also mention location.
We need to avoid duplication. Ensure we don't include boilerplate like "Applications for this position are accepted on an ongoing basis". Also remove "Compensation" heading? We'll make Pay section.
We need to output only HTML fragment.
Let's craft:
About the Team
DoorDash is building the world’s most reliable on-demand logistics engine for delivery! We’re looking for machine learning engineer interns to join our fast-growing engineering team to help us develop a 24x7 global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers.
About the Role
As a Machine Learning Engineer intern at Doordash, you’ll work on tackling new challenges in machine learning and artificial intelligence. You’ll conduct research that can be applied across Doordash engineering teams and engage in external collaborations and mentoring, while also performing research in any of the following areas: Auction, Game theory, Recommender systems, Ranking, AdTech, Computer Vision, Causal Inference, and Big data analytics.
Responsibilities
Qualifications
Pay
Base salary ranges (localized to work location):
In addition to base salary, the compensation package includes opportunities for equity grants. Talk to your recruiter for more information.
Benefits
Schedule
12‑week summer internship program located in our San Francisco, Sunnyvale, New York, or Seattle offices.
Check for any leftover boilerplate: We removed diversity statement, EEO, scam warnings, application instructions, etc. Also removed "About DoorDash" mission statement etc. That is considered boilerplate? The "About DoorDash" paragraph is generic company description; could be considered boilerplate but it's substantive about mission. However the instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The "About DoorDash" mission paragraph is not explicitly listed as boilerplate but could be considered generic. However it's not duplicated. It might be okay to keep? The instruction: "Prefer a few substantive sections over many thin ones." The About DoorDash paragraph is not about the role or team; it's generic company description. Might be considered boilerplate but not explicitly prohibited. Safer to omit? The instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. It does not say to remove generic company description. So we could keep it as part of About the Team