Data Scientist - Model Optimization

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Summary

Location

Burlingame (Hybrid)

Work

Full-time

Experience

5+ years

Key Benefits
Equity Grants
Retirement Plan
Full Health Coverage
Life Insurance
Family Leave
Work From Home

About this Job

Quadric has created an innovative general purpose neural processing unit (GPNPU) architecture. Quadric's co-optimized software and hardware is targeted to run neural network (NN) inference workloads in a wide variety of edge and endpoint devices, ranging from battery operated smart-sensor systems to high-performance automotive or autonomous vehicle systems. Unlike other NPUs or neural network accelerators in the industry today that can only accelerate a portion of a machine learning graph, the Quadric GPNPU executes both NN graph code and conventional C++ DSP and control code.

What We Value:

Integrity, Humility, Happiness

What We Expect:

Initiative, Collaboration, Completion

Role:

You will be joining the data science team that is focused on model optimization, will research, prototype, and validate low‑precision techniques that make neural networks leaner and faster on the Chimera GPNPU. Your analyses will set the quantization recipes that ship in the Chimera SDK and influence future hardware features.

Responsibilities:

  • Design statistically rigorous experiments to compare PTQ, QAT, pruning, and mixed‑precision schemes on vision, language, and multimodal models.

  • Build calibration datasets; develop Python notebooks/dashboards to track accuracy, latency, power, and memory trade‑offs.

  • Perform layer‑ and token‑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 whitepapers, 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.

  • 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, and statistical calibration.

  • Fluent in Python, PyTorch or TensorFlow, NumPy/Pandas/SciPy, and data‑viz tools (Matplotlib/Plotly).

  • Hands‑on with at least one quantization toolkit (PyTorch FX/PTQ/QAT, TF‑Lite, ONNX‑Runtime, TVM, MLIR Quant).

  • Working knowledge of CNNs, Transformers and DNN architectures

  • Provide competitive salaries and meaningful equity

  • Health Care Plan (Medical, Dental & Vision)

  • Retirement Plan (401k, IRA)

  • Life Insurance (Basic, Voluntary & AD&D)

  • Paid Time Off (Vacation, Sick & Public Holidays)

  • Family Leave (Maternity, Paternity)

  • Work From Home

  • Free Food & Snacks

Founded in 2016 and based in downtown Burlingame, California, Quadric is building the world’s first supercomputer designed for the real-time needs of edge devices. Quadric aims to empower developers in every industry with superpowers to create tomorrow’s technology, today. The company was co-founded by technologists from MIT and Carnegie Mellon, who were previously the technical co-founders of the Bitcoin computing company 21.

  • Quadric is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, religion, sex, national origin, sexual orientation, age, citizenship, marital status, or disability.

About the Company

quadric, Inc logo

quadric, Inc

Privately Held
Transportation & Autonomous VehiclesRobotics Hardware & ComponentsRobotics Software & AI

Quadric has built a unified hardware/software architecture optimized for on-device machine learning inference. Only the Quadric GPNPU (general purpose neural processing unit) delivers high ML inference performance while also running C++ code without forcing the developer to artificially partition application code between two or three different kinds of processors. Quadric's GPNPU is a licensable processor IP core that scales from 1 to 864 TOPs and seamlessly intermixes scalar, vector and matrix code.

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