17 of 17 ONNX Jobs

data pipelines, or distributed software systems and supporting machine learning training or inference pipelines. Familiarity with ML frameworks (PyTorch, TensorFlow), model deployment tools (TensorRT, ONNX, TorchScript) and inference runtimes. Familiarity with Linux-based development environments and production debugging. Experience integrating and debugging complex software systems, ideally in robotic or automated ...

/UAV systems, real-time operating systems, or embedded development. Knowledge of computer vision, SLAM, or sensor fusion. Experience in edge ML deployment (TensorRT, ONNX, quantization, model optimization). Familiarity with cloud/distributed systems (AWS, GCP, Docker, Kubernetes). Open-source contributions or strong public portfolio. Experience ...

cross-functional teams to address engineering challenges and deliver optimized solutions. Required Skills & Experience ML Frameworks : Expert in TensorFlow (TF2/Keras), experience with ONNX Runtime for inference. Programming : Advanced Python for ML; strong understanding of packaging, type checking, and performance profiling. Architecture : Proven experience designing scalable ML systems ...

frameworks such as TensorFlow, Keras, or PyTorch. Exposure to gradient boosting tools such as XGBoost, LightGBM, or CatBoost. Experience with model deployment tools (e.g., ONNX, TensorRT, TensorFlow Serving, TorchServe). Familiarity with ML lifecycle tools such as MLflow, Kubeflow, or Azure ML Pipelines. Experience working with distributed data processing (e.g. ...

MaskRCNN, Transformers) and 3D Vision (PointNet, grasp generation, multi-view geometry, camera calibration). You are proficient with inference optimization tools such as TensorRT, ONNX Runtime, or CUDA to maximize hardware utilization. You have experience curating large-scale datasets, detecting statistical bias, and automating quality assurance within the ML pipeline. ...

e.g., text-to-3D, video generation). Knowledge of NeRFs, Gaussian splatting, or neural scene representations. Experience optimizing models for real-time inference (TensorRT, ONNX, quantization, distillation). GPU programming experience (CUDA, Triton, or similar). Track record of publications at top-tier venues (NeurIPS, CVPR, ICML, ICLR, SIGGRAPH, ECCV ...

model "ladders" across multiple chipsets/device tiers with consistent KPIs and automated regression testing. Experience with embedded inference tooling and runtimes (e.g., TFLite, ONNX Runtime, TensorRT) and model export/compatibility constraints. Familiarity with embedded accelerators and profiling (ARM NEON, DSP/NPU toolchains), kernel/operator tuning ...

similar technical field of study, or you have equivalent knowledge gained through your practical experience. Preferred, but not required: Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks. Experience with embedded systems development for ARM-based system-on-chip architectures. Experience working in a MLOps or DevOps environment. ...

microscope images or biological problems. 3+ years of experience with any of the major deep learning frameworks (PyTorch, Keras, Tensorflow) Experience with ONNX preferred Demonstrable industry experience of delivering computer vision-based AI projects (preferably in biotech) Physical Requirements for this role include: The ability to work on a laptop ...

pipelines. Familiarity with Git and collaborative software development practices. Nice to Have: CUDA kernel development and model optimization (quantization, pruning, distillation). Experience with ONNX, TensorRT, or OpenVINO for deployment. Robotics middleware (ROS2). SLAM, 3D perception, or sensor fusion (LiDAR, IMU). Real-time or low-latency inference pipelines. ...

/Feast/internal) and unified pipelines. Experience with graph frameworks, graph feature engineering, or sequence embeddings. Experience optimizing inference at scale (Triton/ONNX/quantization, batching, caching). The target base salary for this position ranges from $190,800/year to $286,800/year in Zone ...

/C++. 3+ years deploying AI or ML models on embedded systems or constrained hardware. Proficiency with ML inference runtimes such as TensorRT, ONNX Runtime, or TensorFlow Lite. Experience optimizing compute, memory, and power for real-time embedded applications. Familiarity with microcontrollers, embedded Linux, RTOS, or hardware accelerators. Experience with ...

encoding/decoding and file formats Experience with OpenCV, ffmpeg, GPU programming Experience with the raw image camera pipeline and image formats Experience with onnx, coreml, and tensorRT runtime SDKs Interest in photography or videography Compensation will depend on experience and skills, and will also include equity and profit sharing. ...

comparable real-time platforms. Deep understanding of object detection, tracking, temporal modeling, and 3D scene understanding. Experience optimizing models for deployment using CUDA, TensorRT, ONNX, or similar toolchains. Unreal Engine Experience: Proficiency with Unreal Engine, preferably UE4 or UE5. Nice to Have Experience with multi-agent simulation, reinforcement learning environments ...

technical audiences Self-motivated with ability to work independently and collaboratively Required Technical Skills Languages: C++, Java, Python, GoLang ML Stack: PyTorch, ONNX, TensorRT, NVIDIA Triton Infrastructure: Docker, Kubernetes, gRPC DevOps: CI/CD tools, monitoring, logging systems Domains: NLP, Computer Vision, ASR, Video Processing Desired Technical Skills Familiarity with … domains: Natural Language Processing, Computer Vision, Automated Speech Recognition, or Video Processing Knowledge of model formats and optimization (ONNX, TensorRT) Protocol Buffers (protobuf) and gRPC NVIDIA technologies (CUDA, TensorRT, Triton Inference Server) Signal processing techniques and libraries Performance profiling and optimization tools Experience supporting production ML systems Background in high ...

DSPs Apply quantization, pruning, and knowledge distillation to reduce model size and inference latency Use frameworks including TensorFlow Lite Micro, Edge Impulse, ONNX Runtime, and ExecuTorch Integrate ML inference into embedded firmware written in C, C++, or Rust Profile and optimize memory usage, power consumption, and real-time performance • Hybrid … optimization techniques including quantization, pruning, and distillation • Experience deploying models with at least one embedded ML framework such as TFLite Micro, Edge Impulse, or ONNX Runtime • Strong understanding of memory-constrained and power-constrained environments • Excellent problem-solving skills and the ability to work independently and as part ...

rate (FAR) and false rejection rate (FRR) at production-grade thresholds. Cross-platform deployment: Convert and optimise models for CoreML (iOS), TFLite (Android), and ONNX Runtime or equivalent (Desktop). Manage the differences in inference performance across runtimes. Benchmarking: Establish the performance benchmarking pipeline. Verification speed, battery impact … 15fps. Qualifications Tools & proficiency ML Frameworks: PyTorch (expert — primary training), TFLite (expert — Android/iOS inference), CoreML (strong — iOS Neural Engine), ONNX (strong — cross-platform interop), MediaPipe (competent — Android face landmarker integration). Computer Vision & Signal Processing: OpenCV (expert), NumPy/SciPy (expert — rPPG signal processing), dlib (competent), rPPG methods ...