Associate Platform Engineer Jobs
Associate Platform Engineer jobs are open across fintech, cloud services, healthcare technology, and enterprise software, with roles in infrastructure automation, CI/CD pipeline development, and cloud operations, from new-grad to mid-level engineer. Find a role that fits below and apply directly.
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Showing 5 of 2,727+ Associate Platform Engineer jobs
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 JobsAssociate Platform Engineer Job Market
Who's Hiring
- Apple117
- CVS Health63
- Oracle46
- Google44
- JPMorganChase35
Top Industries Hiring
- Technology & Software265
- Insurance41
- Investment & Asset Management30
- Banking & Financial Services27
- Electronics & Hardware25
What Employers Look For
The qualifications that appear most often in associate platform engineer jobs.
- Proficiency with at least one cloud platform such as AWS, GCP, or Azure
- Hands-on experience with containerization tools including Docker and Kubernetes
- Familiarity with infrastructure-as-code tools such as Terraform or Ansible
- Experience building or maintaining CI/CD pipelines using tools like Jenkins or GitLab
- Bachelor's degree in computer science, information systems, or a related technical field
- Basic scripting ability in Python, Bash, or Go for automation and tooling tasks
Tips for Your Associate Platform Engineer Job Search
Tailor your resume for infrastructure keywords
Hiring managers for associate platform engineer roles scan for specific tools: Kubernetes, Terraform, Docker, and CI/CD platforms. List the exact names of tools you've used, not just categories like 'containerization,' so your resume clears automated screening.
Show your work through public repositories
A portfolio of personal or academic projects on a code hosting platform carries real weight for associate-level candidates. Annotate your repositories with README files explaining what each project does and which platform engineering problems it solves.
Apply early to roles that fit
Migrate Mate lists associate platform 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 by deployment environment
Filter openings by the cloud provider or stack the team uses, whether AWS, GCP, or Azure. If your hands-on experience is concentrated in one provider, targeting roles that match reduces the technical gap you'll need to explain in the interview.
Prepare a system design answer at your level
Even associate-level interviews include a simplified system design question. Practice explaining how you'd set up a basic deployment pipeline or containerized service, focusing on the tradeoffs you'd make and why, not on arriving at a perfect architecture.
Negotiate start dates and onboarding support
Once you have an offer, ask about the onboarding structure and whether a mentor or buddy program is in place. For associate platform engineers moving into production environments quickly, knowing the ramp-up support available is a practical part of evaluating any offer.
Associate Platform Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most associate platform engineers?
Apple, CVS Health, and Oracle are hiring the most associate platform engineers right now, with openings concentrated in California, New York, and Texas, based on current listings on Migrate Mate as of November 2026. Demand tends to be strongest at companies with active cloud migration or DevOps modernization initiatives.
How many associate platform engineer jobs are remote?
About 76% of associate platform engineer openings are fully remote or hybrid as of November 2026, reflecting the cloud-native nature of much of the work. Roles focused on infrastructure automation, pipeline maintenance, and monitoring tend to offer the most remote flexibility, while positions requiring hands-on data center work are more likely to require on-site presence.
How do you become an associate platform engineer?
Start by building foundational skills in Linux, networking, and at least one cloud platform through self-study or coursework. Gain hands-on experience with Docker and Kubernetes by deploying personal projects. Pursue a relevant certification such as an AWS or Google Cloud associate credential to validate your skills. Apply to associate or junior-level roles that specify mentorship and structured onboarding.
Can you get an associate platform engineer job with little or no experience?
Yes, but you'll need to demonstrate practical skills through projects rather than job history. Build a portfolio of infrastructure projects, contribute to open-source DevOps tooling, and document what you built and why. Internships, co-ops, or academic capstone projects that involved cloud infrastructure or automation pipelines are strong substitutes for full-time experience when targeting associate-level openings.
What does the associate platform engineer interview process look like?
Most processes include an initial recruiter screen, a technical phone interview covering cloud fundamentals and scripting, and a practical exercise such as a take-home automation task or live coding session. Final rounds typically include a system design conversation scaled to the associate level and a team fit interview. The full process usually runs across two to four weeks from first contact to offer.
Where can I find and apply to associate platform engineer jobs?
You can find and apply to associate platform engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your skills and experience level, then apply directly to each listing that fits.
See All 2,727+ Associate Platform Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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