Senior Software Engineer, Machine Learning Platform - Serving (US)
About The Position
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. It serves internal modelers and engineering teams by providing self-serve infrastructure to bring models into production, adopt open source software and models, and use machine learning at scale. In this role, you’ll build the next generation of the ML serving platform, connecting request routing and online feature retrieval with model inference on CPU and GPU infrastructure. You’ll address latency, reliability, resource-efficiency, and scale challenges, and make platform capabilities accessible through self-serve tools and workflows. Working with experienced platform engineers and internal teams, you’ll own defined projects from technical design and implementation through testing, rollout, and production support. You’ll also help translate open source advances and emerging modeling requirements into measurable production improvements. The role includes building tools, APIs, and workflows for model deployment and operation; integrating inference frameworks and open source models; evolving modular serving architecture; improving Kubernetes deployment and autoscaling; developing adoption, validation, and migration tooling; and improving production performance and reliability through benchmarking, profiling, metrics, tracing, on-call participation, automation, and runbooks.
Requirements
- At least 2 years of software engineering experience building and maintaining production services or infrastructure.
- Strong computer science fundamentals and proficiency in a backend or systems programming language such as Java, Kotlin, Go, C++, or Python.
- Understanding of distributed systems fundamentals, including concurrency, networking, timeouts, failure handling, and performance trade-offs.
- Ability to independently turn a defined infrastructure problem into a technical design, tested implementation, and safe production rollout.
- Experience debugging production systems and using operational data to improve reliability, performance, or cost.
- Ability to collaborate effectively with customers and partners and make complex infrastructure easier to adopt.
- A degree in Computer Science or a related field, or equivalent practical experience.
Nice To Haves
- 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 behavior under production workloads.
- Familiarity with CPU/GPU performance profiling, inference runtimes, or model deployment workflows.
Responsibilities
- Build self-serve tools, APIs, and workflows that enable internal teams to deploy, configure, validate, and operate models independently.
- Evaluate and integrate evolving inference frameworks and open source models, bringing their capabilities into reliable and efficient production services.
- Improve systems for routing prediction requests, retrieving online features, and executing models on a platform serving tens of millions of predictions per second.
- Develop modular components that let request routing, feature retrieval, and model execution evolve and scale independently.
- Improve Kubernetes deployment and autoscaling so workloads respond to changing traffic while meeting latency and availability requirements and using CPU and GPU resources efficiently.
- Build integrations, validation, and migration tools to help teams adopt serving capabilities and the unified global platform.
- Use benchmarking, profiling, metrics, and tracing to identify and address performance bottlenecks.
- Participate in on-call and improve operational automation and runbooks.
- Own defined projects from technical design through implementation, testing, safe rollout, and production support.
- Partner with internal teams to understand modeling requirements, remove adoption barriers, and improve the platform.
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
- Medical, dental, and vision benefits
- 11 paid holidays
- Disability and basic life insurance
- Family-forming assistance
- Mental health program
- Equity grants
- For salaried roles, flexible paid time off/vacation and 80 hours of paid sick time per year
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What This Job Offers
Job Type
Full-time
Career Level
Senior
