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Build and operate the ML infrastructure powering a global AI assistant, including training, deployment, inference, and observability systems in Python and PyTorch/JAX.
Build and deploy AI/computer-vision systems end-to-end, from data pipelines and model training to C++ edge integration and production validation.
Own the roadmap for WEKA’s Augmented Memory Grid, optimizing LLM inference performance by offloading KV-cache and integrating with engines like vLLM and NVIDIA Triton.
Build and optimize AI/ML and data pipelines for a sensor-fusion system, focusing on real-time processing, CI/CD, and MLOps to support an autonomous team in Sydney.
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.
Develops and maintains AI models and data pipelines for mobile game publishing, integrating LLMs and transformer-based systems into scalable game and embedded solutions.
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 optimize distributed systems that run large language models efficiently across Intel hardware, focusing on inference performance, parallelism, and communication.
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.
Design and implement advanced image-processing and computer-vision algorithms (CNNs/SNNs) for next-gen EO/IR camera systems, integrating them into embedded hardware from concept to production.
Lead enterprise GenAI strategy, architect scalable AI systems, and advise C-suite on tech stacks, trade-offs, and ROI for large client engagements.
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.
Build and deploy quantized large language models for in-vehicle AI, focusing on PTQ, QAT, and low-bit inference to ensure numerical consistency and performance on XPENG’s Turing AI chip.
Build and deploy quantized large language models for in-vehicle AI, focusing on PTQ, QAT, and low-bit inference to optimize performance on XPENG’s Turing AI chip.
Builds and optimizes inference stacks for large-scale Apple foundation models, enabling AI features across services like Siri and Photos with low latency.
Our client is a fast-growing AI-driven fintech company building next-generation digital products for the financial services industry. They leverage machine learning and modern cloud technologies to deliver highly…
Optimize AI/ML performance across Apple devices by benchmarking workloads, profiling hardware/software, and driving insights to enhance customer experiences.
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