Lead AI Infrastructure Engineer
At a glance
Senior ML Platform & Ops role at Avride. Austin · no salary stated · we estimate $180,000–$245,000 across the United States.
Avride has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Improve GPU inference performance for onboard systems that need stable, near-real-time response and simulation workloads requiring high throughput
- Take ownership of the GPU inference framework first, then expand into ML infrastructure across the pipelines
- Work closely with applied ML engineers as they shape neural network architectures
- Influence the company's ML infrastructure direction and technical decisions
What you bring
- At least five years of C++ programming experience
- Hands-on experience with PyTorch and a working understanding of GPU operation
- You have diagnosed and fixed performance problems, and built infrastructure such as distributed systems
- You have programmed in multi-threaded settings involving processes, timers and interrupts
Who this fits
This senior engineering role suits someone who can lead through technical decisions and work across ML infrastructure. You must be authorized to work in the United States. The role is based in Austin, with no remote work, relocation support or visa sponsorship.
From the employer
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
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.