Senior Software Engineer, ML Serving Platform

Doordashusa
San Francisco +2 more
On-site

In the 12 months to 30 Jun 2026, Doordashusa filed 230 H-1B positions titled "Software Engineer". Source: U.S. Department of Labor, LCA disclosure data. A record of filings already made, not a commitment to sponsor.

Who this role is best for

Strong fit for backend engineers with distributed systems experience who can build self-serve tools and integrate open source ML infrastructure.

Best fit for

  • Candidates with 2+ years of production infrastructure experience and a passion for distributed systems
    — “You have 2+ years of software engineering experience building and maintaining production services or infrastructure.”
  • Individuals who enjoy solving latency and scalability problems in high-throughput environments
    — “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.”
  • Engineers who can translate open source ML advancements into scalable production services
    — “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.”

Things to consider

  • This role requires hands-on production support and on-call responsibilities
    — “Own performance and reliability in production. Use benchmarking, profiling, metrics, and tracing to identify bottlenecks; participate in on-call”
  • The role demands a focus on resource efficiency and autoscaling in Kubernetes environments
    — “Improve Kubernetes deployment and autoscaling. Help workloads respond to changing traffic while meeting latency and availability requirements and using CPU and GPU resources efficiently.”

How to stand out

  • Highlight experience with Kubernetes autoscaling and performance optimization in your resume
    — “Improve Kubernetes deployment and autoscaling.”
  • Emphasize your history of building self-serve tools and APIs for internal teams
    — “Build tools, APIs, and workflows that help internal teams deploy, configure, validate, and operate models independently”
  • Showcase your ability to evaluate and integrate open source ML frameworks
    — “Evaluate and integrate evolving inference frameworks and enable open source models”
  • Demonstrate your understanding of online feature retrieval and latency-sensitive systems
    — “Experience with ML inference infrastructure, online feature retrieval, or other latency-sensitive distributed services.”
  • Demonstrate a history of using operational data to improve system reliability
    — “You have experience debugging production systems and using operational data to improve reliability, performance, or cost.”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • build tools, APIs, and workflows for self-serve infrastructure
  • evaluate and integrate open source inference frameworks
  • solve demanding inference infrastructure problems
  • help evolve disaggregated serving architecture
  • improve Kubernetes deployment and autoscaling
Typical background
software engineeringbackend developmentdistributed systems

Skills & requirements

Required

Distributed SystemsBackend DevelopmentAPI DesignKubernetesProduction SupportTechnical DesignImplementationTestingOnline Feature RetrievalModel InferenceOpen Source SoftwareSelf-serve InfrastructureProduction Services

Preferred

JavaKotlinGoC++PythonMachine LearningGPU InfrastructureLatency OptimizationReliabilityResource EfficiencyScaleSelf-serve ToolsWorkflowsOpen Source ModelsModular ComponentsDisaggregated ArchitectureInference FrameworksValidationMigration ToolingBenchmarking

Stack & domain

JavaKotlinGoC++PythonDistributed SystemsConcurrencyNetworkingTimeoutsFailure HandlingCollaborationCommunicationTeamworkProblem-solvingLeadershipMachine LearningSoftware EngineeringInfrastructureProduction Services

About the role

Original posting from Doordashusa via Greenhouse

About the Team

DoorDash’s ML Serving Platform delivers tens of millions of predictions per second across DoorDash, Wolt, and Deliveroo, powering search, recommendations, advertising, and logistics. We build request routing, online feature retrieval, and CPU/GPU inference capabilities that help ML and product teams deploy and operate models independently.

About the Role

We’re looking for a hands-on senior engineer to lead core serving investments. We’re especially interested in engineers who have scaled ranking and recommendation systems in production. You’ll make architectural decisions, write production code, and guide delivery with ML, product, and compute infrastructure partners.

Your first 6–12 months will focus on enabling new ranking and recommendation models, including generative approaches, in online production. You’ll resolve bottlenecks with early product partners and turn those solutions into reusable platform capabilities. Success means faster adoption while meeting model quality, latency, reliability, and cost targets.

You’re excited about this opportunity because you will…

Lead projects from customer problem through design, implementation, and rollout. Coordinate delivery across engineers, mentor peers, and help shape the team’s roadmap.

Improve inference performance through production traces, load tests, and CPU/GPU profiling. Work with model owners to balance model quality, tail latency, throughput, and cost.

Partner with Compute on capacity, placement, and scaling, accounting for request complexity, cache state, GPU memory, and model startup behavior.

Build self-serve APIs and deployment workflows that manage models, runtime dependencies, features, and versioned state, with validation, safe rollout, and rollback.

Evaluate, integrate, and extend open source tools and vendor capabilities that advance model serving. Decide when to build or adopt, and collaborate with maintainers and vendors.

Own production reliability through service objectives, observability, on-call, incident response, and follow-through. Reduce recurring operational work through automation.

We’re excited about you because…

You have 6+ years of software engineering experience designing, building, and operating production infrastructure or distributed systems.

You have hands-on experience serving large or complex neural models online, with accountability for latency, availability, and resource efficiency.

You bring strong distributed systems fundamentals and proficiency in a backend or systems language such as Python, Go, Java, Kotlin, or C++.

You understand inference tradeoffs across batching, concurrency, caching, memory, and CPU/GPU utilization, and can diagnose production bottlenecks.

You have operated services on Kubernetes or comparable infrastructure, including resource configuration, autoscaling, and failure recovery.

You’ve led substantial projects through ambiguity, partnered with fast-moving ML or product teams, and helped other engineers grow.

You hold a degree in Computer Science or a related field, or have equivalent practical experience.

Nice to Have

Experience integrating or extending inference tools such as Triton Inference Server or PyTorch.

Experience with multi-GPU inference, model parallelism, model-aware caching, or accelerator scheduling.

Experience building model workflows, deployment tooling, or instrumentation with Metaflow, Helm, or OpenTelemetry.

We value production serving depth and engineering judgment; experience with every technology is not required.

Compensation

The 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 USDAbout 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.

Source: Doordashusa careers (Greenhouse)

Similar roles

More roles at Doordashusa