About the RoleYou'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 KeywordsOpen 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.
CompensationThe 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
