A
AI Engineer, Training Data Infrastructure
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 Build the multimodal training data infrastructure that powers Physical AI systems for autonomous vehicles and robotics. Design and implement large-scale data pipelines that process petabytes of video, sensor, and action data from our global network of annotation experts.
Key Responsibilities
Create multimodal training datasets from diverse robotics and AV data sources
Develop infrastructure for training large multimodal models at scale
Design data processing pipelines for video, LiDAR, radar, and sensor fusion
Build downstream evaluation systems to guide model tuning and dataset composition
Run scaling experiments for model size, dataset quality, and training efficiency
Requirements
3+ years experience scaling and optimizing large AI models
Proficiency in Python with strong software engineering practices
Deep learning fundamentals: optimization, loss functions, neural network architectures
Experience with PyTorch, TensorFlow, or JAX
Strong expertise in distributed computing and parallel processing
Experience with petabyte-scale data processing pipelines
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 Build the multimodal training data infrastructure that powers Physical AI systems for autonomous vehicles and robotics. Design and implement large-scale data pipelines that process petabytes of video, sensor, and action data from our global network of annotation experts.
Key Responsibilities
Create multimodal training datasets from diverse robotics and AV data sources
Develop infrastructure for training large multimodal models at scale
Design data processing pipelines for video, LiDAR, radar, and sensor fusion
Build downstream evaluation systems to guide model tuning and dataset composition
Run scaling experiments for model size, dataset quality, and training efficiency
Requirements
3+ years experience scaling and optimizing large AI models
Proficiency in Python with strong software engineering practices
Deep learning fundamentals: optimization, loss functions, neural network architectures
Experience with PyTorch, TensorFlow, or JAX
Strong expertise in distributed computing and parallel processing
Experience with petabyte-scale data processing pipelines
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