Model quantization jobs

Senior Lead Software Engineer - AI/ML Deep Learning & GPU ML Serving

JPMorgan Chase & Co. · Posted Nov 9, 2026

Open

Hyderabad, Telangana, India
Full-time · Lead
5+ years

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Senior Lead Software Engineer - AI/ML Deep Learning & GPU ML Serving

JPMorgan Chase & Co. · Posted Nov 9, 2026

Open

Hyderabad, Telangana, India
Full-time · Lead
5+ years

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Principal AI Architect

Travoom · Posted Nov 9, 2026

Open

Austin, Texas, United States
On-site
Contract · Principal
10+ years

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Lead the architecture, development, deployment, and ongoing evolution of AI initiatives across a global consumer SuperApp. This is a hands-on technical leadership role focused on open-source foundation models, self-hosted infrastructure, and production AI systems.

Compensation

Compensation is accrued and payable following funding, after which it transitions to a regular salary, equity ownership, and OleX token participation in the overall OleOle group of companies.

Responsibilities

Establish overall AI architecture
Lead initiatives including AskOle
Lead real-time multilingual translation
Develop AI-powered sports history and content generation systems
Develop the sports data engine

Requirements

Nice to have: building consumer AI platforms at scale
Nice to have: multilingual and multimodal AI engineering
Nice to have: Recommendation systems
Nice to have: ranking algorithms
Nice to have: personalization

Skills

Machine learning AI engineering Python PyTorch transformer architectures

Senior Software Engineer — ML Inference & Numerics

Tensordyne · Posted Nov 1, 2026

Open

Munich, Bavaria, Germany
Senior

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Lead software development for LLM inference on custom silicon, focusing on numerical optimization and agentic workflow automation.

Responsibilities

Build agentic workflows to inspect and optimize models
Bring up new models quickly and validate performance on accelerator
Optimize numerics including precision, accumulation, and quantization
Build conversion and emulation software for numerical testing
Ship robust, well-tested software

Skills

Python LLM inference agentic tooling model quantization distributed inference accelerator SDKs ML compilers low-rank factorization sparsity codebooks

Applied AI Engineer – Document Intelligence

Chiri · Posted Nov 5, 2026

Open

United States
5+ years

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Design, build, and deploy an AI-powered document intelligence solution for a Silicon Valley electronics manufacturer in the defense supply chain.

Responsibilities

Design, develop, and maintain the end-to-end RFQ screening pipeline
Build document-processing workflows that extract text from PDFs and render drawing pages as images
Deploy and optimize open-weight multimodal models for fully offline inference on a single GPU server
Select appropriate models and quantization strategies to operate within approximately 48 GB of GPU memory
Develop and refine prompts and extraction logic for quality clauses, material tables, engineering drawings, and technical specifications

Requirements

Nice to have: NIST SP 800-171
Nice to have: CMMC
Nice to have: DFARS 252.204-7012
Nice to have: CUI handling
Nice to have: System Security Plans

Skills

Python LLM vision-language model PDF text extraction OCR

Senior / Staff AI Research Engineer, Real-Time Inference

RoboForce · Posted Nov 5, 2026

Open

Milpitas, California, United States
On-site
Full-time · Senior
$185K–$325K per year
4+ years
Visa sponsorship

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Lead end‑to‑end optimization of embodied AI inference pipelines for edge robotics.

Compensation

Compensation: Salary $185,000–$325,000 USD + Bonus + Equity

Responsibilities

Develop and optimize inference pipelines for embodied AI models targeting real‑time execution on edge hardware
Implement CUDA‑level optimizations including custom kernels, memory layout tuning, and hardware‑aware graph compilation
Apply and advance model compression techniques to achieve production‑grade throughput on constrained devices
Profile and debug end‑to‑end inference stacks using tools such as NSight, TensorRT, and Triton
Collaborate with ML research and robotics teams to co‑design model architectures that meet real‑time control‑loop latency requirements

Requirements

Master's degree in Computer Science, Electrical Engineering, or related field
PhD degree
Nice to have: embodied AI models (VLA, multimodal transformers, diffusion‑based policies)
Nice to have: compiler‑based optimization pipelines such as XLA, torch.compile, MLIR
Nice to have: understanding of robotics system constraints

Skills

CUDA programming GPU architecture

AI Phone - On-device AI / ML Systems Engineer

Bjak · Posted Nov 4, 2026

Open

China

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Responsibilities

Evaluate suitable open-weight and commercially available models for on-device execution.
Benchmark model performance on target mobile hardware.
Integrate models into Android applications and system services.
Optimize inference latency, memory consumption and power usage.
Evaluate quantization, compression and model distillation techniques.

Requirements

Nice to have: on-device LLMs
Nice to have: Qualcomm QNN
Nice to have: MediaTek NeuroPilot
Nice to have: ONNX Runtime
Nice to have: LiteRT

Skills

Python C++ML systems inference engineering model quantization performance optimization hardware constraints inference benchmarks

AI Phone - On-device AI / ML Systems Engineer

Bjak · Posted Nov 4, 2026

Open

Taipei, Taiwan, Taiwan

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Responsibilities

Evaluate suitable open-weight and commercially available models for on-device execution.
Benchmark model performance on target mobile hardware.
Integrate models into Android applications and system services.
Optimize inference latency, memory consumption and power usage.
Evaluate quantization, compression and model distillation techniques.

Requirements

Nice to have: on-device LLMs
Nice to have: Qualcomm QNN
Nice to have: MediaTek NeuroPilot
Nice to have: ONNX Runtime
Nice to have: LiteRT

Skills

Python C++ML systems inference engineering model quantization performance optimization hardware constraints inference benchmarks

AI Phone - On-device AI / ML Systems Engineer

Bjak · Posted Nov 4, 2026

Open

India

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Engineer responsible for evaluating, benchmarking, and integrating on-device AI/ML models into Android applications and system services, optimizing performance and power usage, and developing hybrid local/cloud routing mechanisms.

Responsibilities

Evaluate suitable open-weight and commercially available models for on-device execution
Benchmark model performance on target mobile hardware
Integrate models into Android applications and system services
Optimize inference latency, memory consumption and power usage
Evaluate quantization, compression and model distillation techniques

Requirements

Nice to have: on-device LLMs
Nice to have: Qualcomm QNN
Nice to have: MediaTek NeuroPilot
Nice to have: ONNX Runtime
Nice to have: LiteRT

Skills

Python C++model quantization performance optimization neural network inference hardware constraints

AI Phone - On-device AI / ML Systems Engineer

Bjak · Posted Nov 4, 2026

Open

Seoul, Seoul, South Korea

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The mission is to build proactive applications and devices that bring intelligence into daily conversations, coordination, and workflows with minimal prompting, focusing on on-device AI/ML systems.

Responsibilities

Evaluate suitable open-weight and commercially available models for on-device execution
Benchmark model performance on target mobile hardware
Integrate models into Android applications and system services
Optimize inference latency, memory consumption and power usage
Evaluate quantization, compression and model distillation techniques

Requirements

Nice to have: on-device LLMs
Nice to have: Qualcomm QNN
Nice to have: MediaTek NeuroPilot
Nice to have: ONNX Runtime
Nice to have: LiteRT

Skills

ML systems or inference engineering deploying and optimizing models Python C++neural network inference model quantization

AI Phone - On-device AI / ML Systems Engineer

Bjak · Posted Nov 4, 2026

Open

Remote, United Kingdom
Remote

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The role involves building proactive AI applications and devices that bring intelligence into daily workflows, focusing on on-device AI/ML systems.

Responsibilities

Evaluate suitable open-weight and commercially available models for on-device execution
Benchmark model performance on target mobile hardware
Integrate models into Android applications and system services
Optimize inference latency, memory consumption and power usage
Evaluate quantization, compression and model distillation techniques

Requirements

Nice to have: On-device LLMs
Nice to have: Qualcomm QNN
Nice to have: MediaTek NeuroPilot
Nice to have: ONNX Runtime
Nice to have: LiteRT

Skills

ML systems Inference engineering Python C++Neural network inference Model quantization Performance optimization

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