Platform Software Engineer Jobs
Platform Software Engineer jobs are open across fintech, cloud infrastructure, enterprise SaaS, and healthtech, from new-grad to staff and principal levels, with specializations in developer experience, internal tooling, and distributed systems reliability. Find a role that fits below and apply directly.
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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.
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.
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.
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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Find JobsPlatform Software Engineer Job Market
Who's Hiring
- Apple68
- Oracle55
- JPMorganChase40
- Amazon24
- ServiceNow19
Top Industries Hiring
- Technology & Software68
- Science & Research7
- Electronics & Hardware6
- Banking & Financial Services6
- Insurance3
What Employers Look For
The qualifications that appear most often in platform software engineer jobs.
- Proficiency in Kubernetes, container orchestration, and cloud infrastructure on AWS, GCP, or Azure
- Experience building and maintaining CI/CD pipelines using tools such as GitHub Actions, Jenkins, or Argo
- Strong programming skills in Go, Python, or Java for building shared internal services and tooling
- Familiarity with infrastructure-as-code frameworks such as Terraform, Pulumi, or AWS CDK
- Experience defining and operating service-level objectives and reliability practices for distributed systems
- Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent experience
Tips for Your Platform Software Engineer Job Search
Tailor your resume for platform scope
Platform roles care about blast radius and reliability at scale, not just feature delivery. Rewrite your bullet points to show how many engineers or services consumed your platform work, and what toil you eliminated for them.
Highlight your on-call and SLO ownership
Most platform software engineer job descriptions expect you to own uptime targets, not just build toward them. If you've defined SLOs, led incident retrospectives, or reduced MTTR on a shared service, call that out explicitly on your resume.
Apply early to roles that fit
Migrate Mate lists platform software engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target companies with platform engineering teams
Not every company separates platform engineering from product engineering. Search job descriptions for words like 'developer productivity,' 'internal developer platform,' or 'golden path' to find teams doing real platform work rather than general backend roles relabeled as platform.
Prepare a system design answer for internal platforms
Platform interviews lean heavily on designing shared infrastructure: CI/CD pipelines, secrets management, container orchestration, or service meshes. Practice explaining trade-offs between build-vs-buy decisions, since interviewers often probe whether you understand total cost of ownership for internal tooling.
Negotiate scope and team structure, not just title
At platform-focused companies, whether you report into an infrastructure org or a product org changes your roadmap influence significantly. During offer negotiation, ask who the platform team's internal customers are and how roadmap priorities get set.
Platform Software Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most platform software engineers?
The most active employers for platform software engineers right now are Apple, Oracle, and JPMorganChase, and the most openings are in California, Washington, and New York, based on current listings on Migrate Mate as of November 2026. Demand is highest at companies running large-scale microservices architectures that need dedicated teams to maintain internal developer tooling and infrastructure.
How many platform software engineer jobs are remote?
About 78% of platform software engineer openings are fully remote or hybrid as of November 2026, reflecting broad acceptance of distributed infrastructure work. Developer experience, internal tooling, and observability sub-specializations tend to be the most remote-friendly, while roles that involve hands-on data center or hardware-adjacent work typically require more on-site presence.
How do you become a platform software engineer?
Start by building strong foundations in distributed systems, containerization, and at least one cloud provider. Move into roles that give you ownership of shared services rather than product features, such as a backend or DevOps position on a team with infrastructure responsibilities. Progressively take on SLO ownership, on-call rotation leadership, and cross-team API design to build the platform-specific depth that hiring managers look for.
Can you get a platform software engineer job with little experience?
Yes, entry-level platform software engineer roles exist, though they're less common than mid-level openings. Employers hiring junior candidates look for demonstrated exposure to containerization, cloud deployments, and scripting even if gained through internships, open-source contributions, or personal projects. Building a public project that provisions and operates a small distributed service is one of the most effective ways to demonstrate readiness without years of experience.
What does the platform software engineer interview process look like?
Most processes include a recruiter screen, a technical phone or video interview focused on systems concepts, and a full loop with rounds covering distributed systems design, coding in your primary language, and a discussion of past infrastructure projects. Many companies also include a scenario round where you're asked to diagnose a reliability incident or evaluate trade-offs in a proposed platform architecture.
Where can I find and apply to platform software engineer jobs?
You can find and apply to platform software engineer jobs on Migrate Mate, which lists current openings from companies across the United States. Find roles that match your experience and specialization, then apply directly to each listing from the page.
See All 1,002+ Platform Software Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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