Software Engineer – ML Platform

ABOUT THE TEAM

The ML Platform team at Avride builds the infrastructure that powers large-scale

ML training and data processing for autonomous driving. We sit between Cloud

Platform and ML engineers, turning low-level compute, storage, and networking

primitives into an ML platform that teams actually use — scalable orchestration,

distributed compute, and production-grade tooling for the full model lifecycle.

ABOUT THE ROLE

As an ML Platform Engineer at Avride, you'll own critical pieces of the ML

stack: workflow orchestration, distributed execution, resource governance,

performance.You will shape how ML teams across the company run experiments and

train models at scale. You will build the abstractions and services that make

training workloads reliable, cost-efficient, and fast, helping ML teams run at

scale on Kubernetes with strong reliability and excellent developer experience.

WHAT YOU WILL DO

* Build and scale our ML compute platform on Kubernetes, using Argo Workflows

for training, evaluation, and data processing orchestration

* Design and implement core platform capabilities, including a Ray-based

internal SDK for distributed execution, and multi-tenant resource governance

— scheduling, priorities, quotas, and policy enforcement across GPU, CPU,

memory, and IO

* Improve end-to-end training throughput and platform efficiency by optimizing

data access patterns, caching, and removing bottlenecks in storage, network,

and resource contention

* Work directly with ML teams to debug complex workload issues, drive

root-cause analysis, and turn recurring problems into platform-level fixes

* Evaluate, integrate and extend open-source tooling (Argo Workflows, Ray,

Kubernetes ecosystem) to meet evolving platform needs

WHAT YOU WILL NEED

* Strong proficiency in Python or Go; C++ is a plus

* Track record of designing and building scalable, maintainable systems and

services

* Experience operating production services end-to-end: APIs, reliability

practices, observability

* Deep knowledge of Kubernetes: how scheduling, resource management,

controllers, and pod lifecycle actually behave under pressure

* Solid Linux and systems debugging skills: performance investigation,

networking, storage/IO

* Ability to troubleshoot complex production issues across logs, metrics, and

traces and drive them to resolution

NICE TO HAVE

* Experience with Argo Workflows, Ray, MLflow, or comparable distributed ML

tooling

* Hands-on experience building or operating large-scale ML training systems:

GPU scheduling, distributed training, training data pipelines

* Track record of optimizing resource usage and performance in distributed

environments

Candidates are required to be authorized to work in the U.S. The employer is not

offering relocation sponsorship, and remote work options are not available.

Avride is an equal opportunity employer and committed to providing reasonable

accommodations to qualified applicants and employees with disabilities to ensure

they have equal access to employment opportunities. Avride complies with the

Americans with Disabilities Act (ADA), if you need a reasonable accommodation to

assist with the application or hiring process, or to perform the essential

functions of a job, please email [email protected] [[email protected]].