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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, ML Serving Platform - **Company:** DOORDASH, INC. - **Location:** Seattle, WA, United States (Remote available) - **Experience:** Experienced - **Salary:** $130,600.0 - $192,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), C++ (Programming Language), Profiling, Software Debugging, Distributed Systems, Python (Programming Language), Machine Learning, Operational Data Store, Open Source Technology, Software Engineering, System Programming, Graphics Processing Unit (GPU), Autoscaling, Concurrency, Backend, Kotlin, Containerization, Kubernetes, Information Technology, Low Latency, Free and Open-Source Software, Build Tools, Machine Learning Operations, Hardware Infrastructure, Model Inference, Golang - **Published:** October 30, 2026 - **Apply:** https://www.careerbuilder.com/job-details/software-engineer-ml-serving-platform-seattle-wa--813b1583-3b66-41f0-850a-803239ecb037 ## About the Role Software Engineering, Delivery Driving, Advertising, Food Delivery, Logistics, Open Source, Machine Learning, Ecosystems, Energy Efficiency, GPU (Graphics Processing Unit), * 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. ## Description 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 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 to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only We used Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provided Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023. We resumed using Covey Scout for Inbound again on June 29, 2024, and ceased using Covey Scout for Inbound on April 30, 2026. The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: https://getcovey.com/nyc-local-law-144.