Job description

About the Team 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 #LI-MS1 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 jobs@avride.ai .

Requirements

Must-haves

About the Team
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
#LI-MS1 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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlai

Platform: greenhouse
Effort: quick