Hands-on experience with large-scale language models (LLMs) and prompt engineering (e.g., GPT, BERT, T5 family). Familiarity with on-device or edge-AI deployments (e.g., TensorFlow Lite, ONNX, mobile/embedded inference). Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance. Open-source contributions or published papers in top-tier More ❯
computer vision and a strong understanding of model architectures like transformers and CNNs. Hands-on experience with model optimization (i.e. quantization, pruning) and model deployment frameworks such as TensorRT, ONNX Runtime, and OpenVINO. Proficiency with CUDA programming and optimizing code for GPU acceleration. Strong background in MLOps practices, including CI/CD using GitHub Actions and containerization with Docker. Excellent More ❯
experience communicating methodological choices and model results. • Demonstrated experience with verification and validation test benches. • Demonstrated experience with Explainable AI (XAI) techniques. • Demonstrated experience with OpenNeural Net Exchange (ONNX). More ❯
computer vision and a strong understanding of model architectures like transformers and CNNs. Hands-on experience with model optimization (i.e. quantization, pruning) and model deployment frameworks such as TensorRT, ONNX Runtime, and OpenVINO. Proficiency with CUDA programming and optimizing code for GPU acceleration. Strong background in MLOps practices, including CI/CD using GitHub Actions and containerization with Docker. Excellent More ❯
computer vision and a strong understanding of model architectures like transformers and CNNs. Hands-on experience with model optimization (i.e. quantization, pruning) and model deployment frameworks such as TensorRT, ONNX Runtime, and OpenVINO. Proficiency with CUDA programming and optimizing code for GPU acceleration. Strong background in MLOps practices, including CI/CD using GitHub Actions and containerization with Docker. Excellent More ❯
model robustness on diverse datasets. Leading the full lifecycle of model development, from research and training to validation and performance benchmarking. Mastering model export to various formats such as ONNX, OpenVINO, and TensorRT to support a wide range of hardware. Working on model deployment strategies, including optimizing models for high-performance inference on both cloud and edge devices. Contributing to More ❯
model robustness on diverse datasets. Leading the full lifecycle of model development, from research and training to validation and performance benchmarking. Mastering model export to various formats such as ONNX, OpenVINO, and TensorRT to support a wide range of hardware. Working on model deployment strategies, including optimizing models for high-performance inference on both cloud and edge devices. Contributing to More ❯
Experience working with a modern cloud service (AWS, GCP, Azure etc.) Nice to Have Hands-on experience with autonomous driving systems Experience with model deployment with NVIDIA stack (e.g. ONNX graphs, TensorRT, profiling) Familiarity with recent breakthroughs in ML (e.g. foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures) Knowledge of autonomous driving, large-scale data curation pipelines More ❯
Experience working with a modern cloud service (AWS, GCP, Azure etc.) Nice to Have Hands-on experience with autonomous driving systems Experience with model deployment with NVIDIA stack (e.g. ONNX graphs, TensorRT, profiling) Familiarity with recent breakthroughs in ML (e.g. foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures) Knowledge of autonomous driving, large-scale data curation pipelines More ❯
Experience working with a modern cloud service (AWS, GCP, Azure etc.) Nice to Have Hands-on experience with autonomous driving systems Experience with model deployment with NVIDIA stack (e.g. ONNX graphs, TensorRT, profiling) Familiarity with recent breakthroughs in ML (e.g. foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures) Knowledge of autonomous driving, large-scale data curation pipelines More ❯
Experience working with a modern cloud service (AWS, GCP, Azure etc.) Nice to Have Hands-on experience with autonomous driving systems Experience with model deployment with NVIDIA stack (e.g. ONNX graphs, TensorRT, profiling) Familiarity with recent breakthroughs in ML (e.g. foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures) Knowledge of autonomous driving, large-scale data curation pipelines More ❯
environments Preferred Qualifications: Experience building ML models with wearable data (e.g., continuous heart rate, motion, respiration) Exposure to embedded AI or edge model deployment (e.g., TensorFlow Lite, Core ML, ONNX) Knowledge of healthcare data privacy and security (e.g., HIPAA, GDPR) Familiarity with GMLP (Good Machine Learning Practice) and clinical evaluation frameworks Seniority level Seniority level Not Applicable Employment type Employment More ❯
implementation. Extensive experience with common machine learning Python frameworks such as TensorFlow and PyTorch; and Python libraries such as pandas, and computer vision libraries such as OpenCV. Experience in ONNX and TensorRT. Very comfortable working in Linux environment. Familiarity with software development tools and agile development practices. 6 years experience in developing, optimizing, and testing deep learning in computer vision More ❯
shutter control. Familiarity with sensor fusion concepts and real-time data synchronization across sensors. Experience integrating or optimizing AI/ML models on embedded edge devices (e.g., TensorFlow Lite, ONNX on Jetson, Coral, or NPU-based SoCs). Basic knowledge of embedded Linux platforms (Yocto, U-Boot, kernel-level familiarity as needed). Proficiency in Python or MATLAB for testing More ❯
instruments. Responsibilities Develop control software for our custom hardware stack (cameras, actuators, LEDs, fluidics). Build and synchronise image acquisition pipelines for multi-channel CMOS sensors. Deploy TorchScript/ONNX models for edge inference on embedded compute nodes. Design APIs and UI layers for translating AI output into researcher-friendly results. Take ownership of parts of the stack from device More ❯
bottlenecks, and optimizing them Good understanding of docker and containerization (Good to have) experience with Pytorch and Python3, and comfortable with C++ (Good to have) Understanding of Torch script, ONNX runtime, TensorRT (Good to have) Understanding of half-precision inference and int8 quantization What we offer 80-150k base Company equity % in an early-stage startup 100% company-paid More ❯
of two of the following compiler areas: Front-end - handle the handshaking of common Deep Learning Frameworks with Gensyn's IR for internal IR usage. Write transformation passes in ONNX to alter IR for middle-end consumption. Middle-end - write compiler passes for training-based compute graphs, integrate reproducible Deep Learning kernels into the code generation stage, and debug compilation More ❯
of two of the following compiler areas: Front-end - handle the handshaking of common Deep Learning Frameworks with Gensyn's IR for internal IR usage. Write transformation passes in ONNX to alter IR for middle-end consumption. Middle-end - write compiler passes for training-based compute graphs, integrate reproducible Deep Learning kernels into the code generation stage, and debug compilation More ❯
Preferred Qualifications: Experience building ML models with wearable data (e.g., continuous heart rate, motion, respiration). Exposure to embedded AI or edge model deployment (e.g., TensorFlow Lite, Core ML, ONNX). Knowledge of healthcare data privacy and security (e.g., HIPAA, GDPR). Familiarity with GMLP (Good Machine Learning Practice) and clinical evaluation frameworks. #J-18808-Ljbffr More ❯
Whetstone, Greater London, UK Hybrid / WFH Options
all.health
Preferred Qualifications: Experience building ML models with wearable data (e.g., continuous heart rate, motion, respiration). Exposure to embedded AI or edge model deployment (e.g., TensorFlow Lite, Core ML, ONNX). Knowledge of healthcare data privacy and security (e.g., HIPAA, GDPR). Familiarity with GMLP (Good Machine Learning Practice) and clinical evaluation frameworks. The successful candidates starting pay will be More ❯
Demonstrated academic or professional experience communicating methodological choices and model results. • Demonstrated experience with verification and validation test benches. • Demonstrated experience with Explainable AI (XAI) techniques. • Demonstrated experience with ONNX (OpenNeural Net Exchange) Salary Range: $150,000-$200,000 All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national More ❯
stacks, and hardware architectures PREFERRED EXPERIENCE: Strong Python and C/C++ programming skills Deep understanding of AI/ML algorithms, tools/frameworks, and model representations such as ONNX and PyTorch Experience in analytical modeling of ML operators on target architectures, focusing on compute and data movement Background in using optimization libraries and solvers (e.g., PuLP, CBC, Gurobi) is More ❯
a multiplatform inference backend. Our remote team is committed to advancing AI technology and making it broadly accessible. Responsibilities: Deploy machine learning models on edge devices using llama.cpp, ggml, onnx frameworks. Collaborate with researchers to code, train, and transition models to production. Integrate AI features into existing products, leveraging latest ML advancements. Requirements: Proficiency in Python, C, and C++ programming. More ❯
Cambridge, Cambridgeshire, United Kingdom Hybrid / WFH Options
Arm Limited
collaborating with remote teams across different time zones. "Nice To Have" Skills and Experience : Experience of Windows WDDM or Linux kernel driver development. Experience of AI frameworks (TensorFlow, PyTorch, ONNX). Knowledge of multimedia use cases including video, camera, display, and GPU. In Return: You will have the opportunity to demonstrate technical expertise and leadership skill to engage with GPU More ❯
