# Data Scientist - Model Optimization

**Company:** [Quadric](https://hotfix.jobs/companies/quadric)
**Location:** Burlingame, CA
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** Python, PyTorch, TensorFlow, NumPy, pandas, Scipy, Matplotlib, Plotly, Pytorch Fx, Ptq, Qat, Tf-Lite, Onnx-Runtime, Tvm, Mlir Quant
**Posted:** 2026-04-02

> Develops custom quantization algorithms and prototypes for optimizing ML models on Quadric's GPNPU hardware. Requires MS/PhD, 5+ years in model optimization, deep quantization expertise, and Python/PyTorch proficiency.

## Job Description

## Responsibilities
- Design statistically rigorous experiments to compare PTQ, QAT, and mixed-precision schemes on vision, language, and multimodal models.
- Implement custom quantization algorithms from scratch, adapting existing techniques or developing novel approaches to match Chimera GPNPU's unique architectural features and numerical formats.
- Build calibration datasets; develop Python notebooks/dashboards to track accuracy, latency, power, and memory trade-offs.
- Perform layer-level error analysis to guide numerical-format choices.
- Partner with compiler team to convert your findings into turnkey SDK flows and reference configs.
- Publish internal white papers, external benchmarks, and present results to customers and at industry events.
- Monitor academic literature in compression and efficient inference; translate promising ideas into reproducible prototypes.

## Requirements
- M.S./Ph.D. in CS, EE, Applied Math, or similar, with 5+ years in ML model optimization or data-science-driven research.
- Deep grasp of fixed-point arithmetic, quantization theory, numerical analysis, and statistical calibration.
- Strong ability to implement quantization algorithms from first principles, not just use existing frameworks.
- Fluent in **Python**, **PyTorch** or **TensorFlow**, **NumPy**/**Pandas**/**SciPy**, and data-viz tools (**Matplotlib**/**Plotly**).
- Experience implementing custom quantizers and understanding their interaction with hardware constraints (bit-width, format, operations).
- Hands-on with at least one quantization toolkit (**PyTorch FX/PTQ/QAT**, **TF-Lite**, **ONNX-Runtime**, **TVM**, **MLIR Quant**) and ability to extend them.
- Working knowledge of CNNs, Transformers, and DNN architectures.

## Bonus
- Experience with custom hardware accelerators, DSPs, or neural processing units.

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