Software Engineer, SystemsML, Training Frameworks & Inference Optimization

The Core AI Model Optimization and Research (MORE) team is hiring SWEs to work on Model Optimization and Training Frameworks. The MORE team is a horizontal ML team under XRTech consisting of multiple pillars focusing on:

1. Training Frameworks and Training Efficiency
2. Model Optimization for On-Device Inference
3. Foundational On-Device Models.

Our team enables on-device models across Family of Apps, VR and AR, bringing the magic of AI to the edge. Along with our partners, we have shipped on-device SAM on IG and Full body Avatars on Quest-3. We also build and maintain the training framework (Vizard) used by multiple groups in XRTech for model development and optimization.Software Engineer, SystemsML, Training Frameworks & Inference Optimization Responsibilities

• Optimize models for latency and power consumption for both on-device and GPU inference.
• Work closely with partner teams to help them meet quality, latency and complexity constraints on resource constrained devices.
• Build tools to automate model optimization and compression.
• Fine tune, quantize and deploy models on phones, AR and VR devices.
• Contribute to Vizard, a new framework built for training models targeted towards on-device use-cases.
• Ideate and implement new features in Vizard to improve developer experience and efficiency.
• Drive framework adoption by supporting migration of key training workloads into the framework.
• Support periodic oncall and help resolve user issues.
• Work with the Vizard team to improve reliability and efficiency of the framework.

Minimum Qualifications

• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
• Specialized experience in the following machine learning/deep learning domains: Model quantization, compression, on-device inference, GPU inference, PyTorch

Preferred Qualifications

• Masters/PhD degree in Computer Science, Computer Engineering or relevant technical field.
• Experience in building ML frameworks (Pytorch, Pytorch Lightning).
• Experience with distributed systems and optimizing resource utilization.
• Experience with accelerating deep learning models for on-device inference.
• Familiarity with on-device inference platforms (ARM, Qualcomm DSP).
• Optimizing machine learning model inference and training on NVIDIA GPUs.

LocationsAbout Meta Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics. Meta is committed to providing reasonable support (called accommodations) in our recruiting processes for candidates with disabilities, long term conditions, mental health conditions or sincerely held religious beliefs, or who are neurodivergent or require pregnancy-related support. If you need support, please reach out to accommodations-ext@fb.com. $117,000/year to $173,000/year + bonus + equity + benefits

Individual pay is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base salary, Meta offers benefits. Learn more about benefits at Meta.