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Design and deploy ML systems (CNN, RNN, LSTM), optimize MLOps pipelines, and run distributed training using Spark and Kubernetes in a fully remote, short-term contract role.
Design and deploy advanced ML models (CNNs, RNNs, LSTMs) using Kubernetes and MLOps pipelines; optimize models for scale with distributed training and Apache Spark.
The AI Research Engineer bridges applied research and production engineering by designing and deploying advanced machine learning systems. The role involves building scalable ML pipelines, staying current with AI research, and optimizing models for real-world business applications.
The GPU Software Engineer will design and optimize high-performance CUDA kernels for AI and scientific computing workloads. The role involves profiling GPU code, collaborating with ML teams to improve training and inference pipelines, and working with modern accelerator hardware.
The LLM Engineer will design and operationalize fine-tuning workflows for large language models using techniques like RLHF and DPO. The role involves building scalable training pipelines, managing GPU cluster operations, and collaborating with cross-functional teams to deliver production-grade AI solutions.
SDE Intern at Annapurna Labs building software—compilers, ML runtimes, firmware, and drivers—for Amazon's custom silicon (Graviton, Trainium, Inferentia, Nitro) using C, C++, and Python.
Senior ML Engineer at Criteo AI Lab in Paris designing, developing, and deploying large-scale recommendation and prediction systems for the advertising platform using Python, PyTorch, and distributed training infrastructure.
As a Senior CPU Expert for NVIDIA's CSP team, you will drive CPU technical strategy, solution optimization, and customer engagement with major Chinese cloud providers. Core technologies include CPU architecture (x86, ARM), C/C++, Python, Linux kernel/virtualization, and NVIDIA's CPU-GPU-DPU platforms.
This role involves architecting and optimizing end-to-end training workflows for robotics foundation models like Cosmos and GR00T. The candidate will work with researchers and engineers to scale multimodal model training and data pipelines across multi-GPU and multi-node systems.
Design, build, and operate GPU cluster and platform infrastructure for large-scale AI training and inference workloads, including scheduling, high-performance storage, and networking, using PyTorch, JAX, DeepSpeed, Kubernetes/Slurm, Python, and Go/C++.
Principal AI/ML Scientist driving enterprise-level AI solutions across lululemon's retail ecosystem—spanning personalization, forecasting, and generative AI—using Python, PyTorch, cloud ML platforms, and distributed training systems.
Senior Staff ML Engineer building and scaling ML infrastructure for LLM training, evaluation, and deployment at Moveworks (ServiceNow), working with PyTorch, vLLM, TensorRT-LLM, Python, and C++/GoLang.
Develop and implement ML-based prediction and planning algorithms for Nuro's Level 4 autonomous driving platform using Python, C++, and deep learning techniques like reinforcement learning and transformers.
An internship developing AI-driven autonomy software for Nuro’s Level 4 self-driving platform, focusing on data platforms, ML infrastructure, simulation, or technical infrastructure to enable scalable autonomous mobility solutions.
Builds and maintains the ML infrastructure platform for Nuro’s autonomous vehicle AI, focusing on GPU/CPU resource provisioning, workload orchestration, and petabyte-scale data pipelines to accelerate model development from experimentation to production.
Build and scale the ML infrastructure platform that provides compute and data resources for autonomous vehicle development, using IaC, workload orchestrators (Kubernetes, Ray), and distributed data processing frameworks.
Build and maintain ML infrastructure for Nuro’s autonomous driving models, including distributed GPU training, data pipelines, and Kubernetes-native orchestration to enable safe, scalable self-driving vehicles.
Build and maintain ML infrastructure for Nuro’s autonomous driving models, including distributed GPU training, data pipelines, and orchestration systems to keep autonomy development running smoothly.
Builds mid-training strategies for large multimodal AI models to improve reasoning, planning, and tool-use capabilities at scale.
Builds and optimizes speech and audio capabilities for multimodal AI models, focusing on ASR, TTS, and real-time systems from data pipelines to production deployment.
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