A
AI Engineer, Data Scaling & Optimization
Avala
5 hours ago
Full-time
On-site
San Francisco, California, United States
Posted 10 months ago San Francisco, CA full-time
About This Role Scale Avala's neural network training infrastructure to handle the massive data requirements of Physical AI. Optimize training pipelines, implement efficient data loading, and ensure our ML systems can leverage petabytes of multimodal robotics and AV data.
Key Responsibilities
Design and implement high-performance neural network training infrastructure
Optimize training and evaluation code for maximum efficiency
Build data pipelines handling petabytes of autonomous vehicle and robotics data
Develop profiling and debugging tools for ML training systems
Implement distributed training across large GPU clusters
Work with research teams to enable rapid experimentation at scale
Requirements
4+ years experience with ML infrastructure and training systems
Deep expertise in PyTorch or TensorFlow internals
Strong understanding of distributed computing and GPU optimization
Experience with large-scale data processing (petabyte+)
Proficiency in Python and C++ for performance-critical code
Knowledge of modern ML architectures (transformers, vision models)
Experience optimizing memory usage and training throughput
What We Offer Competitive salary and meaningful equity in the team building Fleet Learning Infrastructure for Physical AI
Full health, dental, and vision coverage, plus FSA and unlimited PTO
Work on the closed-loop data infrastructure that the next generation of autonomous systems learns from
A team that built Tesla Autopilot's data infrastructure, now building something bigger
#J-18808-Ljbffr
About This Role Scale Avala's neural network training infrastructure to handle the massive data requirements of Physical AI. Optimize training pipelines, implement efficient data loading, and ensure our ML systems can leverage petabytes of multimodal robotics and AV data.
Key Responsibilities
Design and implement high-performance neural network training infrastructure
Optimize training and evaluation code for maximum efficiency
Build data pipelines handling petabytes of autonomous vehicle and robotics data
Develop profiling and debugging tools for ML training systems
Implement distributed training across large GPU clusters
Work with research teams to enable rapid experimentation at scale
Requirements
4+ years experience with ML infrastructure and training systems
Deep expertise in PyTorch or TensorFlow internals
Strong understanding of distributed computing and GPU optimization
Experience with large-scale data processing (petabyte+)
Proficiency in Python and C++ for performance-critical code
Knowledge of modern ML architectures (transformers, vision models)
Experience optimizing memory usage and training throughput
What We Offer Competitive salary and meaningful equity in the team building Fleet Learning Infrastructure for Physical AI
Full health, dental, and vision coverage, plus FSA and unlimited PTO
Work on the closed-loop data infrastructure that the next generation of autonomous systems learns from
A team that built Tesla Autopilot's data infrastructure, now building something bigger
#J-18808-Ljbffr