Software Engineer, SI Infrastructure

Software Engineer, Super Intelligence InfrastructureTesla's Super Intelligence Infrastructure team builds the software that turns GPUs and custom silicon into reliable, efficient training capacity for Autopilot, Optimus, Robotaxi, and Dojo. We own the systems layer between bare metal and researchers: cluster lifecycle, fleet automation, observability, scheduling interfaces, and the control-plane software that keeps multi-node training jobs healthy at scale.In this role, you'll work as a software engineer in a high-performance environment, shipping production services and tooling that operate thousands of accelerated machines, not only configuring them by hand. You'll design, build, and operate software that provisions, monitors, heals, and optimizes our SI training fleets.

You treat the cluster as a product: APIs, automation pipelines, reliability signals, and abstractions that make supercomputing capacity safe and fast for ML users. You'll focus on distributed-systems and systems-software problems (hosts, accelerators, fabric, storage, schedulers), then encode fixes into software so the same failure class doesn't require a hero page again. Scope and level scale with impact, from owning major automation surfaces to leading design across fleet-wide platforms.ResponsibilitiesKeep the training fleet up; Drive reliability and efficiency of GPU clusters so research and production training progress is not gated on infrastructure toil or silent degradationShip automation as product; Replace manual ops with reliable, testable software, APIs, job pipelines, operators/controllers, and libraries other engineers and on-call can depend onOwn observability and remediation paths; Build metrics, logging, alerting, and automated remediation (e.g.

Prometheus/Grafana-based stacks) that detect bad nodes, sick GPUs, fabric/storage faults, and scheduler failures and fence or repair safely at scaleRaise training reliability through software; Model failure domains, reduce blast radius of HW/SW faults on long-running multi-node jobs, and improve effective capacity (less idle accelerators, fewer silent stragglers)Develop performance-oriented systems tooling; Build and extend tools that measure and improve throughput/stability across OS, network, storage, and runtime layers for real distributed training workloadsIntegrate with scheduling and workload platforms; Engineer against and improve HPC/SI scheduling and orchestration surfaces (e.g. Slurm/LSF and related platforms; containers/K8s where used) so capacity is allocatable, debuggable, and efficientPartner across the stack working with ML infrastructure users, networking, data center, and hardware/vendor partners to bring up capacity and expose clean software interfaces, not ad-hoc runbooks aloneParticipate in a 24×7 rotation for critical training infra; invest in SLIs/SLOs, runbooks-as-code, and permanent fixes that drive pages down over timeRequirementsStrong software engineering instincts applied to infrastructure: you write maintainable automation and services, not only operate ticketsProficiency in Python and/or Bash (additional languages a plus); comfort building tools others will run in productionDeep Linux experience in production (boot/image or config management, networking stack, performance debugging, package/driver realities on real hardware)GPU cluster operations or SI training infrastructure (NVIDIA stack, driver/CUDA ecosystem familiarity, multi-node job behavior)HPC schedulers and workload managers (Slurm, LSF, or similar); exposure to Kubernetes for ML/infra workloads a plusHigh-performance networking: RDMA, RoCEv2, InfiniBand, congestion behavior, and host-side tuning for collective-heavy trainingParallel / high-performance storage (Lustre, GPFS/Spectrum Scale, BeeGFS, or cloud/parallel equivalents) and its failure modes under training I/OExperience with configuration management / IaC-style automation at fleet scale (e.g. Ansible, or equivalent)Willingness to participate in a 24×7 on-call rotation for critical SI training infrastructure (cadence shared during hiring; escalation and automation are first-class goals of the role)Degree in CS, EE, or related field, or equivalent practical experienceCompensation & BenefitsBenefitsAlong with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:Medical plans > plan options with $0 payroll deductionFamily-building, fertility, adoption and surrogacy benefitsDental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contributionCompany Paid (Health Savings Accounts) HSA Contribution when enrolled in the High-Deductible medical plan with HSAHealthcare and Dependent Care Flexible Spending Accounts (FSA)401(k) with employer match, Employee Stock Purchase Plans, and other financial benefitsCompany paid Basic Life, AD&DShort-term and long-term disability insurance (90 day waiting period)Employee Assistance ProgramSick and Vacation time (Flex time for salary positions, Accrued hours for Hourly positions), and Paid HolidaysBack-up childcare and parenting support resourcesVoluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insuranceWeight Loss and Tobacco Cessation ProgramsTesla Babies programCommuter benefitsEmployee discounts and perks programExpected Compensation$140,000 - $252,000/annual salary + cash and stock awards + benefitsPay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience.

The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.