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Job Details: Job Description: About the Organization As part of Intel's CTO Office, you will join a vertically integrated incubation effort dedicated to bringing Intel's neuromorphic technology innovations to…
RELOCATION ASSISTANCE: Relocation assistance may be available CLEARANCE REQUIRED FOR START: Yes CLEARANCE TYPE: Top Secret TRAVEL: Yes, 10% of the Time Description At Northrop Grumman, our employees have incredible…
Build and deploy optimized large language models for autonomous vehicles, focusing on quantization (PTQ/QAT), inference pipelines, and performance benchmarking on XPENG’s Turing AI chip.
Build and deploy quantized large language models for autonomous-vehicle inference, focusing on PTQ/QAT, mixed-precision, and runtime integration with PyTorch and TensorRT-LLM.
Build and deploy AI/ML models for vision, audio, and language tasks using PyTorch/TensorFlow and cloud MLOps tools.
Own the integration layer between AI/ML models and physical robotics systems, implementing ROS 2 nodes, industrial fieldbus protocols, and real-time control loops to bridge model inference with hardware like motion controllers and actuators.
Owns the full ML lifecycle for physical AI, from sensor data pipelines to deploying optimized models on constrained hardware, collaborating with embedded teams to ensure reliability and performance in real-world devices.
Build low-level compilers, runtimes, and AI/DSP optimizations for next-gen hardware in C++/Python, working closely with hardware teams.
Design and run Absa’s multi-cloud AI platform (AWS Bedrock, Databricks, Azure AI Foundry) that powers 43 live AI projects across ten countries, focusing on FinOps, zero-trust security, agentic AI infrastructure, and enterprise-grade observability.
Build and run a secure, multi-cloud AI platform for a bank, deploying services on AWS Bedrock, Databricks, Azure AI, Hugging Face and Kubernetes while optimizing costs, security and observability for enterprise-scale AI workloads.
Build and deploy AI/computer-vision systems end-to-end, from data pipelines and model training to C++ edge integration and production validation.
Research and implement model quantization algorithms to optimize AI models for on-device deployment using PyTorch, ONNX, and related tools.
R&D intern on Nota’s AI team optimizing models for deployment on edge devices using PyTorch, ONNX, and frameworks like ExecuTorch/TensorRT.
Research and develop quantization, pruning, and inference optimizations for LLM/VLM and MoE models to run efficiently on GPUs and NPUs.
Build and scale AI-powered software products and automation solutions using TypeScript/NestJS, Python, and AWS for enterprise clients, owning features end-to-end from API design to deployment.
Build and maintain embedded firmware for telematics and video devices that power fleet-management insights, optimizing safety, cost, and sustainability for thousands of vehicles.
Build and deploy end-to-end ML systems for industrial sorting machines, focusing on computer vision and MLOps across cloud and edge.
Build and ship computer-vision models: train, evaluate, wrap in services, and demo via simple web UIs. Core stack: Python, PyTorch, OpenCV, FastAPI/Flask, Docker.
Build and maintain a Rust-based platform that deploys and orchestrates AI models on edge devices, optimizing for constrained hardware and integrating with cloud services.
Build and deploy AI models for edge devices: port vision/speech models to NPU-enabled SoCs, extend AI tooling for hardware-aware development, and create reference designs for smart-home and surveillance use cases.
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