Data Scientist, New Grad - Model Optimization

Responsibilities

• Develop and deploy quantization workflows for vision and language models, taking models from FP32 reference to deployable low-precision implementations that meet accuracy targets.
• Investigate per-layer numerical error, identify accuracy regressions, and propose calibration, PTQ, or QAT strategies to recover lost accuracy.
• Extend Quadric's quantization library with new operators, observers, and algorithms.
• Build and maintain numerical accuracy testing infrastructure and debug tooling for neural networks running on the GPNPU.
• Collaborate with graph compiler, kernel, and hardware teams to co-design solutions that exploit the GPNPU's numerical capabilities.

Requirements

• B.S., M.S., or Ph.D. in CS, EE, Applied Math, or a related field, completed within the last year.
• Strong Python skills and fluency with PyTorch (or TensorFlow), NumPy, and data-viz tools (Matplotlib/Plotly).
• Solid machine learning foundations; working knowledge of CNNs and Transformers.
• Interest in quantization, numerical representation, fixed-point arithmetic, or low-level performance.
• Ability to read research papers, evaluate the core ideas, and reproduce key results.

Nice-to-Haves

• Hands-on experience with quantization or model compression, or any of PyTorch FX/PTQ/QAT, TF-Lite, ONNX-Runtime, TVM, or MLIR Quant.
• Experience with embedded systems, DSPs, GPUs, or other accelerators.
• Published research, open-source contributions, or coursework projects in model optimization, efficient ML, or systems for ML.

Compensation & Benefits

• Competitive salary and meaningful equity.
• Medical, dental, and vision plan options starting on day one.
• 401(k) retirement plan.
• Flexible paid time off (unlimited, non-accrual).
• Company-provided lunches and stocked kitchen when working in-office.
• Support for commuting, including monthly parking or Caltrain passes.