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Practising for the listing Senior Software Engineer, Machine Learning Platform - Serving (US). Its requirements are filled in below. When you have an answer you like, paste it with your CV in Career Mirror to see which of the job's skills it shows.

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Senior Software Engineer, Machine Learning Platform - Serving (US)
About the Team

DoorDash’s ML Serving Platform delivers tens of millions of predictions per second , powering search, recommendations, advertising, delivery estimates, and logistics across DoorDash, Wolt, and Deliveroo.

Our customers are modelers and engineering teams across our internal business verticals. We build self-serve infrastructure that empowers them to bring new models into production, adopt open source software and models, and expand what they can accomplish with machine learning at scale.

We work at the intersection of a rapidly evolving open source ecosystem and demanding production workloads. With active customer demand and growing modeling ambitions, we’re advancing the infrastructure behind today’s predictions while building the capabilities that enable the next generation of ML innovation.

About the Role

You’ll help build the next generation of our ML serving platform, connecting request routing and online feature retrieval with model inference on CPU and GPU infrastructure. You’ll tackle challenging infrastructure problems involving latency, reliability, resource efficiency, and scale, and make those capabilities accessible through self-serve tools and workflows.

Working alongside experienced platform engineers, you’ll own defined projects from technical design and implementation through testing, rollout, and production support. You’ll partner directly with internal teams to understand emerging modeling requirements, remove adoption barriers, and turn advances in open source technology into measurable production improvements.

You’re excited about this opportunity because you will…

• Empower modelers through self-serve infrastructure. Build tools, APIs, and workflows that help internal teams deploy, configure, validate, and operate models independently, accelerating ML adoption across business verticals.

• Bring open source innovation into production. Evaluate and integrate evolving inference frameworks and enable open source models, translating promising capabilities into reliable, efficient services at scale.

• Solve demanding inference infrastructure problems. Improve the systems that route prediction requests, retrieve online features, and execute models on a platform serving tens of millions of predictions per second.

• Help evolve our disaggregated serving architecture. Build modular components that allow routing, feature retrieval, and model execution to evolve and scale independently as workloads and modeling requirements change.

• Improve Kubernetes deployment and autoscaling. Help workloads respond to changing traffic while meeting latency and availability requirements and using CPU and GPU resources efficiently.

• Make adoption and rollout easier. Build integrations, validation, and migration tooling that help teams adopt new serving capabilities and our unified global platform with confidence.

• Own performance and reliability in production. Use benchmarking, profiling, metrics, and tracing to identify bottlenecks; participate in on-call and improve automation and runbooks to make the platform easier to operate.

We’re excited about you because…

• You have 2+ years of software engineering experience building and maintaining production services or infrastructure.

• You have strong computer science fundamentals and proficiency in a backend or systems programming language such as Java, Kotlin, Go, C++, or Python.

• You understand distributed systems fundamentals, including concurrency, networking, timeouts, failure handling, and performance trade-offs.

• You enjoy challenging infrastructure problems and can independently turn a defined problem into a technical design, tested implementation, and safe production rollout.

• You have experience debugging production systems and using operational data to improve reliability, performance, or cost.

• You care about the engineers using your platform and collaborate effectively with customers and partners to make complex infrastructure easier to adopt.

• You hold a degree in Computer Science or a related field, or have equivalent practical experience.

Nice to Have

• Experience with ML inference infrastructure, online feature retrieval, or other latency-sensitive distributed services.

• Experience operating containerized workloads on Kubernetes, including deployment, resource management, or autoscaling.

• Experience building self-serve developer platforms, APIs, or automation that helps other engineers move faster.

• Experience integrating open source infrastructure or models and validating their behavior under production workloads.

• Familiarity with CPU/GPU performance profiling, inference runtimes, or model deployment workflows.

Relevant Technologies and Keywords

Open source inference runtimes and models, NVIDIA Triton, ONNX Runtime, Kubernetes, CPU/GPU inference, gRPC, online feature stores, distributed systems, performance profiling, autoscaling, and self-serve ML infrastructure.

Familiarity with every technology is not expected. We welcome engineers with strong backend and infrastructure fundamentals who are excited to learn the ML serving ecosystem.
Compensation

The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.

In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.

DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 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). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.

To learn more about our benefits, visit our careers page here .

See below for paid time off 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).

The national base pay range for this position within the United States, including Illinois and Colorado.
$167,800 — $246,800 USD

About DoorDash

At 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 by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order 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 Inclusion

We’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.

Notice Regarding Use of AI and Automated Tools: To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Metaview.

How it works: Metaview assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Metaview or other AI-enabled tool in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law.

Data Retention, Privacy & Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy https://help.doordash.com/en-us/legal/article/ax-privacy-notice and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Metaview is publicly available for review at https://trust.warden-ai.com/metaview/application-review .

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Requirements found

Logistics Python Marketing (must) Compliance Excel Recruiting (must) Training

What this posting asks for

Level: Senior — The title says "Senior".

Main responsibilities

Build tools, APIs, and workflows that help internal teams deploy, configure, validate, and operate models independently, accelerating ML adoption across busi…
Improve the systems that route prediction requests, retrieve online features, and execute models on a platform serving tens of millions of predictions per se…
Help evolve our disaggregated serving architecture.
Build modular components that allow routing, feature retrieval, and model execution to evolve and scale independently as workloads and modeling requirements …
Improve Kubernetes deployment and autoscaling.
Help workloads respond to changing traffic while meeting latency and availability requirements and using CPU and GPU resources efficiently.
Build integrations, validation, and migration tooling that help teams adopt new serving capabilities and our unified global platform with confidence.
Own performance and reliability in production.
participate in on-call and improve automation and runbooks to make the platform easier to operate.

Competencies an interviewer will look for

Technical skill Core
In the posting: “Our customers are modelers and engineering teams across our internal business verticals.” · “We build self-serve infrastructure that empowers them to bring new models into production, adopt open source software and models, and expand what they can ac…” · “Help evolve our disaggregated serving architecture.”
Show it by: Names the tools, languages or systems used and how; Explains a technical choice and its trade-off.
Ownership and accountability Core
In the posting: “Build tools, APIs, and workflows that help internal teams deploy, configure, validate, and operate models independently, accelerating ML adoption across busi…” · “Build modular components that allow routing, feature retrieval, and model execution to evolve and scale independently as workloads and modeling requirements …” · “You enjoy challenging infrastructure problems and can independently turn a defined problem into a technical design, tested implementation, and safe productio…”
Show it by: Takes a task from start to finish without being chased; Owns a mistake and what was changed after it.
Customer focus Core
In the posting: “Our customers are modelers and engineering teams across our internal business verticals.” · “With active customer demand and growing modeling ambitions, we’re advancing the infrastructure behind today’s predictions while building the capabilities tha…” · “You care about the engineers using your platform and collaborate effectively with customers and partners to make complex infrastructure easier to adopt.”
Show it by: Puts the customer's need in their own words; Shows a result for the customer, not only for the company.
Data and analysis Core
In the posting: “Use benchmarking, profiling, metrics, and tracing to identify bottlenecks;” · “You have experience debugging production systems and using operational data to improve reliability, performance, or cost.” · “Data Retention, Privacy & Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy https://help.doordash.co…”
Show it by: Uses a number to describe the situation or the result; Explains what the data showed and what was done with it.
Attention to detail and quality Core
In the posting: “Working alongside experienced platform engineers, you’ll own defined projects from technical design and implementation through testing, rollout, and producti…” · “That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental…” · “In compliance with New York City Local Law 144, the independent bias audit summary for Metaview is publicly available for review at https://trust.warden-ai.c…”
Show it by: Gives an example of an error caught before it caused harm; Names the check or standard they used.
Problem solving Core
In the posting: “Solve demanding inference infrastructure problems.” · “DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, a…”
Show it by: Describes a real problem, the options considered and the one chosen; Shows how the cause was found, not just the fix.
Stakeholder management Core
In the posting: “You care about the engineers using your platform and collaborate effectively with customers and partners to make complex infrastructure easier to adopt.” · “At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that …”
Show it by: Shows how they aligned people with different goals; Explains how they handled a 'no' from someone senior.
Teamwork and collaboration Supporting
In the posting: “You care about the engineers using your platform and collaborate effectively with customers and partners to make complex infrastructure easier to adopt.”
Show it by: Says what they did inside a team, not only what 'we' did; Shows how a disagreement was handled.
Planning and organisation Supporting
In the posting: “That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental…”
Show it by: Shows how priorities were set when everything was urgent; Mentions deadlines met and how.
Adaptability and learning Supporting
In the posting: “For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.”
Show it by: Describes learning something new quickly and using it; Shows a change of plan and how they adjusted.

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Practice questions

Tell me about yourself. Dedicated step-by-step practice.
If an interviewer asks something improper How to respond to questions about age, family, origin or health.
Built from this posting (8):
Walk me through a piece of work where you used your strongest technical skill. What exactly did you do? From this posting
Tell me about a task you took from start to finish without anyone asking you to. From this posting
Describe a time you went out of your way to solve a customer's problem. From this posting
Tell me about a time you used data or numbers to make a decision. From this posting
How have you used marketing in your work? Walk me through one real example. From this posting
Tell me about a problem you solved using recruiting. From this posting
Tell me about a time you helped someone on your team get better at their work. From this posting
Describe a time you worked with another team to deliver something. What was your part? From this posting
This posting reads as Software engineering: its questions come first.
Walk me through a feature you built from start to finish. What tools and languages did you use? Software engineering
Tell me about the hardest bug you have tracked down. How did you find the cause? Software engineering
Describe a technical design decision you made and the trade-off behind it. Software engineering
Tell me about a time you disagreed with a teammate in a code review. Software engineering
Production is down and you are the first engineer to notice. What do you do in the first 30 minutes? Software engineering
How do you decide what to test before you ship a change? Software engineering
Tell me about a time you helped a less experienced engineer get unstuck. Software engineering
Your estimate for a feature turns out to be badly wrong halfway through. What do you do? Software engineering
Tell me about a time a shipment or delivery went wrong.
How have you used spreadsheets in your work?
Describe a time you handled an upset customer or colleague.
Tell me about a time you had several deadlines at once. How did you decide what to do first?
Give an example of a mistake you caught before it caused a problem.
Tell me about a time you taught someone how to do something.
Describe a time you took the lead without being asked.
Tell me about a time you had to explain something complicated in simple terms.
Describe a process you improved.
Do you have any questions for us?
Why do you want to work here?
Why is there a gap in your employment? Difficult question
Why did you leave your last job? Difficult question
Why are you changing careers? Difficult question
What is your greatest weakness? Difficult question
What are your salary expectations? Difficult question
You seem overqualified for this role. Why do you want it? Difficult question
Improper-question practice: How old are you? / Do you have children? / Where are you from originally?
A customer is angry about a delay you did not cause. What do you do?
How do you keep track of many open customer requests?
Tell me about a time you could not give a customer what they wanted.
You find that the inventory count does not match the system. What do you do?
How do you make sure safety rules are followed on a busy shift?
Two managers ask you to book the same room at the same time. What do you do?
Tell me about a time you had to enter or check a lot of data accurately.
Tell me about a sale or agreement you are proud of.
A prospect says your price is too high. How do you respond?
How do you keep your pipeline organised?
Tell me about a technical problem you solved on your own.
You find a bug right before a release. What do you do?
Walk me through how you would check whether a report's numbers are right.