Lead AI Infrastructure Engineer
Avride
Salary undisclosed
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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.
Benefits
- remote work
About this role
Avride's self-driving stack depends on a foundational infrastructure layer, and this role puts you in charge of its most critical piece: GPU inference performance. You'll own the ML infrastructure that powers both real-time onboard autopilot execution and high-throughput simulation environments, working directly with applied ML teams to optimize neural network operations across the company's autonomy pipeline.
This is a leadership-oriented position that starts with deep technical work on GPU inference frameworks—diagnosing latency issues for onboard systems and throughput bottlenecks for offboard processing—then expands into broader ownership of ML infrastructure across pipelines. You'll need 5+ years of C++ experience, strong GPU fundamentals, and a proven track record building distributed systems and tackling performance problems at scale. Familiarity with PyTorch and hands-on experience in multi-threaded, multi-process environments are essential.
The role is based in Austin and requires U.S. work authorization; remote work is not available despite the job posting's initial remote label.
How this employer is doing
solid
- H1B: Sponsors Visas
- Expanding
- Sponsors Visas
This role's local market on JobMinglr
Pay for this role
The employer didn't post a pay range for this role. That usually means pay is set in negotiation, which favors whoever arrives with numbers. Check ranges on comparable Lead AI Infrastructure Engineer postings in Austin, and analyze any offer before you accept it.
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