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About Hark Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and…
Lead the design, training, and on-device deployment of audio ML models for wake-word detection, voice enhancement, and speech processing in Hark’s AI-powered hardware products.
Build and maintain ML pipelines that convert and deploy autonomous-truck models to edge hardware, ensuring latency and accuracy meet safety-critical requirements.
Builds and maintains the inference engine layer for AI models, designing benchmarking suites and validating partner solutions across hardware targets (data centers/edge). Core focus: performance, correctness, and reproducibility of model deployments using frameworks like llama.cpp and ONNX.
Research and implement hardware-aware neural network optimization techniques (quantization, pruning, NAS) and optimize ML inference pipelines and compilers/runtimes (TVM, MLIR, TensorRT, ONNX Runtime) for edge devices at Huawei's Software-Hardware System Optimization Lab in Edmonton.
The Senior Computer Vision Engineer will design and deploy real-time perception algorithms for autonomous aerial platforms. The role involves working with C++, Python, and deep learning models to optimize performance on embedded hardware for field-tested robotics applications.
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9…
Optimize and deploy ML models for embedded automotive autonomy, balancing model quality with hardware efficiency using quantization, mixed-precision, and hardware-aware techniques.
Design and deploy ML models directly onto custom hardware, co-architecting solutions with traders and engineers while optimizing for latency and resource constraints.
Крупнейший универсальный коммерческий банк Казахстана приглашает тебя в свою команду. Мы ищем талантливых людей, готовых развиваться и расти вместе с нами. Группа Halyk – это более 17 000 сотрудников в Казахстане и…
Build and integrate AI/LLM features (RAG, agents, search, automation workflows) into the My OPSWAT customer portal, owning the model lifecycle from data pipelines to self-hosted LLM deployment and evaluation.
About the Role VESSL AI의 Backend Software Engineer (Senior)는 GPU 클라우드 플랫폼의 설계와 구현을 리드합니다. VESSL은 여러 데이터센터와 클라우드에 걸쳐 H200·B300부터 GB300 NVL72, Vera Rubin에 이르는 최신 GPU 클러스터를 운영하며, 이를 효과적으로 활용하기 위해 Kubernetes 기반 컨테이너부터 VM,…
Product area Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by…
The AI Embedded Engineer IV integrates AI models onto autonomous vehicle hardware, managing the deployment, optimization, and real-time performance of robotics software. This role balances software development in C++ and Python with hands-on hardware tasks like sensor integration, wiring, and field testing.
The Lead AI Engineer will design and deploy production-grade computer vision and speech processing pipelines for Digital Green's multilingual agricultural advisory platform, FarmerChat. This role involves building optimized inference systems, defining evaluation frameworks, and mentoring junior engineers to support smallholder farmers globally.
Edge AI Engineer designs, optimizes, and deploys machine learning models on resource-constrained edge devices using model compression, quantization, and hardware-aware optimization techniques with frameworks like TensorFlow Lite, ONNX Runtime, and Core ML.
The Machine Learning Infrastructure Engineer will design and maintain high-performance inference platforms for large machine learning models, focusing on systems engineering tasks like request routing, autoscaling, and GPU optimization. The role requires expertise in Python, systems programming languages, and production-grade AI serving frameworks.
Optimize LLM/VLM inference performance on NVIDIA GPUs—profiling workloads, building/tuning CUDA kernels, and improving open-source inference engines like TensorRT-LLM and vLLM.
Build NVIDIA NIM's model customization and deployment lifecycle platform—designing fine-tuning pipelines (LoRA, quantization), evaluation harnesses, and compliance/attestation layers on top of LLM serving infrastructure.
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