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 ❯
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 ❯
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 ❯
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 ❯
from recent ML papers to solve practical challenges using retail image datasets Benchmark multiple approaches through rigorous experimental pipelines Build scalable inference pipelines using frameworks such as PyTorch, TensorRT, ONNX, and TensorFlow Lite (cloud and edge) Collaborate with engineers and product stakeholders to integrate models into end-to-end systems Perform error analysis and iterate on models continuously in production … models such as diffusion models and vision-language models (VLMs) Proficient in Python and familiar with ML frameworks like PyTorch Familiarity with model optimization and inference acceleration tools (e.g., ONNX, TensorRT, OpenVINO, vLLM) Demonstrated ability to ship ML models in production settings, including performance monitoring and failure analysis Excellent communication skills and proven ability to collaborate across cross-functional teams More ❯
