Who we are

Kinara is a Bay Area-based venture backed company. Our architecture is based on research done at Stanford University by Rehan Hameed and Wajahat Qadeer under the guidance of legendary Prof. Mark Horowitz (http://www.vlsi.stanford.edu/~horowitz/) and Prof. Christos Kozyrakis (http://csl.stanford.edu/~christos/).

What we do

• Hot startup delivering on Generative AI Semiconductor play for the edge
• Patented technology developed by founders during Ph.D at Stanford
• Best in-class Silicon performance, power, Only Edge AI company shipping in volume
• Peerless software tool suite that’s a game changer for the company
• Well funded, Marquee (GAFA) customers including top e-tailer and Tier 1 PC OEM
• Two generations of Silicon shipping in volume, mature software stack
• Well capitalized, Series B led by Tiger Global, TSMC, Western Digital, Stanford, Catchlight

Job Summary

We’re lookingfora skilled and motivated Machine Learning Software Engineer with 2- 5 years of experience to join ourteam. The ideal candidate will have a solid foundation in deep learning and a strong interest in optimizing and deploying ML models on specialized hardware. This role involves implementing model optimizations, with a particular focus on quantization, to improve the performance of machine learning inference on target platforms.

Key Responsibilities

• Model Porting s Deployment: Port and deploy deep learning models from frameworks like PyTorch and TensorFlow to proprietary or commercial ML accelerator hardware platforms.
• Performance Optimization: Analyze and improve the performance of ML models for target hardware, focusing on latency andthroughput
• Quantization: Contribute to model quantization efforts (e.g., INT8) to reduce model size and accelerate inference while maintaining model accuracy.
• ProfilingsDebugging: Use profiling tools to identify and fix performance bottle necks in the ML inference pipeline on the accelerator.
• Define and document interface specifications, control/status logic, and pipeline structures.
• Lead PPA analysis and trade-off discussions across RTL and architecture.

Necessary Qualifications

• Experience: 2-5 years of professional experience in software engineering, with a focus on machine learning model deployment and optimization


TechnicalSkills:

1. Proficiency in deep learning frameworks such as PyTorch and TensorFlow.
2. Hands-onexperience with deploying and optimizing models on GPUs or other specialized accelerators.
3. Some experience with model quantization (Post-Training Quantization).
4. Strong proficiency in C++ and Python.
5. Experience with GPU programming models like CUDA/cuDNN is a plus.
7. Familiarity with ML inference engines and runtimes (e.g., TensorRT, OpenVINO, TensorFlow Lite).
8. Foundational understanding of computer architecture principles.
• Version Control: Proficient with Git and collaborative development workflows.
• Education: Bachelor’s or Master’s degree in Computer Science, Electrical
  Engineering, or a related field.

Preferred Qualifications:

• Knowledge of hardware-aware model design.
• Familiarity with compilertechnologies for deep learning.
• Experience with real-time or embedded systems.
• Knowledge of cloud platforms (AWS, GCP, Azure).
• Experience with CI/CD pipelines for ML models.