Lead AI Infrastructure Engineer

Avride Inc.; Austin (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Avride is seeking a software engineer with leadership experience to drive the ML infrastructure layer across the company. You will lead GPU inference optimization for onboard and high-throughput offboard scenarios, while guiding broader ML infrastructure across pipelines.

The role requires deep C++ experience, strong multi-threading skills, and collaboration with the applied ML team to align neural model architectures with performance goals.

Qualifications

• Experience with PyTorch and GPU architectures.
• Proven ability to diagnose and resolve performance issues in large systems.
• Strong track record building distributed infrastructure.

Responsibilities

• Own the GPU inference framework with a focus on performance.
• Take ownership of broader ML infrastructure across pipelines.
• Collaborate with the applied ML team on model architecture and deployment.

Skills

PyTorch

GPUs

Performance debugging

Infrastructure

C++

Multi-threading

Job description

Our team is at the core of Avride's self-driving stack. We build the base infrastructure layer that powers all autopilot code. It includes a C++ framework for implementing autonomy components, execution graph building and optimization systems, as well as runtimes that execute those graphs, both onboard and in simulation.

The vast part of the execution graph is implemented as a chain of neural network operations. The onboard mode relies on stable latencies of the inference of those networks, while in simulation we also optimize throughput at scale.

About the role

We’re looking for a software engineer with a leadership mindset and deep ML infrastructure experience. You will decide and influence the ML infrastructure layer across the company. The biggest challenge we’re facing at the moment is the effectiveness of GPU inference - both for onboard applications with near real-time guarantees and for offboard cases that target high throughput and deterministic execution. It is the first priority within this role.

What you'll do

• At first, you will take on the GPU inference framework, focusing on performance
• Later, the role assumes responsibility and ownership for broader ML infrastructure scattered across ML pipelines
• Close collaboration with the applied ML team responsible for defining the neural model's architecture

What you'll need

• Experience with PyTorch
• Understanding of how GPUs work
• Experience in diagnosing and resolving performance issues
• Strong record of building infrastructure including distributed systems
• 5+ years of experience with C++
• Programming experience in multi-threaded environments - multiple processes, threads, timers, and interrupts

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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 [e-mail] .