Tesla

Makes electric cars, robotaxis and grid batteries

Staff Software Engineer - Infrastructure Engineering

Platform EngineeringConfirmed live in the last 24 hours

Full-timeAustin, TX, USANo salary listedIn-person

About the Job

Requirements

Have a minimum of 8+ years of practical experience as a backend or platform engineer building scalable distributed systems, ideally operating at tech lead level, cloud-native products, developer tools, or external developer-facing products.
Have built agent sandbox or ephemeral compute platforms using gVisor, Kata, Firecracker, or similar technologies.
Have strong fundamentals in service-oriented architectures, networking, and systems design, ideally having owned both the technical vision and execution of a foundational platform system end to end.
Have strong proficiency in Python, Go, Rust, or similar systems languages (full stack).
Have production experience with KServe, Triton, vLLM, or Ray Serve for model inference.
Have built or operated training-as-a-service, distributed training, MLflow, or job scheduling.
Apply root cause analysis and systems thinking to failure modes, back-pressure, resource contention, and blast radius.
Have deep expertise in Kubernetes internals, including the scheduler, API server, etcd, admission controllers, CRDs, and control plane at scale.
Have built production Kubernetes operators in Go using controller-runtime or Kubebuilder.
Have production Cluster API (CAPI) experience, including management clusters, custom providers, ClusterClass, and fleet-scale lifecycle.

Responsibilities

Build and own the end-to-end SI/ML platform for training, inference, and experimentation as a self-service product for internal users.
Write production Kubernetes operators and controllers in Go for GPU workloads, training jobs, model deployments, sandboxes, and cluster lifecycle.
Operate large-scale GPU fleets (A100/H100/B200), including scheduling, MIG, topology-aware placement, and health monitoring.
Build and operate inference infrastructure using KServe, Triton, vLLM, and Ray Serve, including autoscaling, model versioning, and request batching.
Build and operate training-as-a-service, including distributed training (PyTorch, FSDP), MLflow, and checkpoint management.
Design secure, isolated sandbox environments for SI agents and ephemeral execution contexts for untrusted code using gVisor, Kata, or Firecracker.
Architect serverless/Lambda-style ephemeral workloads, scale-to-zero, and event-driven compute using Knative, KEDA, or Firecracker microVMs.
Integrate GPU compute, training, inference, and sandboxes as first-class services in the internal cloud platform.
Manage Kubernetes cluster lifecycle at fleet scale using Cluster API (CAPI) for provisioning, upgrades, scaling, and decommissioning.

Category	Engineering & Information Technology
Location	AUSTIN, Texas
Requisition ID	281111
Type	Full-time

Job Description

This team manages multiple functions across Tesla that includes Platform Engineering managing fleet of Kubernetes clusters, Devops, MLOps, Cloud Infrastructure, Sandbox platform and Factory SRE as well. Continued development and automation of deployment, monitoring, self-healing, and alerting processes is imperative to the success of our engineering groups. <br><br>You will be responsible for the internal platform that lets every team securely spin up sandboxed Super Intelligence agents, run ephemeral workloads, train models at scale, and serve them in production all as a reliable, self-service experience. This is a high-leverage, high-ownership role where you will design, build, and operate the systems that sit at the intersection of Kubernetes, high-performance networking, GPU infrastructure, and modern ML platforms.

Job Responsibilities

Build and own the end-to-end SI/ML platform (training, inference, experimentation) as a self-service product for all internal users
Write production Kubernetes operators and controllers in Go for GPU workloads, training jobs, model deployments, sandboxes, and cluster lifecycle
Operate large-scale GPU fleets (A100/H100/B200) scheduling, MIG, topology-aware placement, health monitoring
Build and operate inference infrastructure using KServe, Triton, vLLM, and Ray Serve autoscaling, model versioning, and request batching
Build and operate training-as-a-service; distributed training (PyTorch , FSDP), MLflow, and checkpoint management
Design secure, isolated sandbox environments for SI agents and ephemeral execution contexts for untrusted code (gVisor, Kata, Firecracker)
Architect serverless/Lambda-style ephemeral workloads, scale-to-zero, event-driven compute (Knative, KEDA, Firecracker microVMs)
Integrate GPU compute, training, inference, and sandboxes as first-class services in the internal cloud platform
Manage Kubernetes cluster lifecycle at fleet scale using Cluster API (CAPI) provisioning, upgrades, scaling, and decommissioning

Job Requirements

Have a minimum of 8+ years of practical experience as a backend or platform engineer building scalable distributed systems, ideally operating at tech lead level , cloud-native products, developer tools, or external developer facing products
Built agent sandbox/ephemeral compute platforms (gVisor, Kata, Firecracker, or similar)
Strong fundamentals in service-oriented architectures, networking, and systems design, ideally having owned both the technical vision and execution of a foundational platform system end to end
Strong profciency in Python, Go, Rust, or similar systems languages (full stack)
Production experience with KServe, Triton, vLLM, or Ray Serve for model inference
Built or operated training-as-a-service, distributed training, MLflow, job scheduling
Root cause analysis and systems thinking - failure modes, back-pressure, resource contention, blast radius
Deep Kubernetes internals expertise (scheduler, API server, etcd, admission controllers, CRDs, control plane at scale)
Built production Kubernetes operators in Go (controller-runtime / Kubebuilder)
Production Cluster API (CAPI) experience; management clusters, custom providers, ClusterClass, fleet-scale lifecycle

About the Company

Tesla

Makes electric cars, robotaxis and grid batteries

Tesla makes electric cars and batteries that store power for homes and utilities, and it develops self-driving software and humanoid robots. In 2023, its Model Y became the first electric vehicle to rank as the world's best-selling car. Buyers order the Model 3 sedan, Model Y SUV, or Cybertruck pickup directly from Tesla instead of through dealers, and they charge on its Supercharger fast-charging network. It also sells Megapack and Powerwall batteries, runs a robotaxi service, and charges drivers a monthly fee for self-driving software that still needs their supervision. Tesla was founded in 2003, with Elon Musk as its lead early investor and a recognized co-founder, and it is based in Austin, Texas.

Company size: 10,001+
Company stage: IPO
Headquarters: Austin, TX
Founded: 2003

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