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Build and improve multimodal AI models that understand and generate text, images, and video for real-time user experiences.
Research and develop large-scale multimodal AI pretraining systems, optimizing data pipelines and training infrastructure to advance foundation models for real-time, intuitive intelligence.
Build and run the distributed GPU infrastructure that trains and serves PhysicsX’s large AI models for engineering simulation, partnering with research scientists to optimize training pipelines and model deployment.
Principal ML Infrastructure Engineer at PhysicsX in London builds and operates AI-driven simulation infrastructure for training, fine-tuning, and serving large physics models, using NVIDIA DGX clusters and PyTorch.
About us PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack…
This role involves building infrastructure, pipelines, and models for video technology across Apple products. The engineer will work on the full ML lifecycle, including training, performance optimization, and production deployment.
Role: Kubernetes Platform Engineer Location: Santa Clara, CA(Onsite) AI Infrastructure The Candidate will provide senior Kubernetes platform engineering services for AI infrastructure environments supporting model…
This role involves building and scaling generative world models to produce synthetic data for autonomous driving research. You will bridge the gap between ML research and engineering by optimizing model architectures and integrating synthetic data into the training pipeline for driving models.
Build and optimize large-scale ML training and inference pipelines for low-latency trading systems using Python, CUDA, PyTorch/TensorFlow, and GPU acceleration.
Designs and sells high-performance GPU and HPC cloud solutions for AI workloads, translating customer needs into feasible architectures while collaborating with engineering teams.
The Software Engineer will join the AI Libraries team to build and maintain the platforms, libraries, and tools that enable ML engineers to train and scale autonomous driving models. The role focuses on creating stable, scalable infrastructure using Python and cloud technologies to support large-scale machine learning development.
Designs and scales machine learning infrastructure to evaluate autonomous vehicle driving behaviors, focusing on large-scale model training and distributed systems for Waymo’s fleet.
Build and scale large-scale ML infrastructure for Reddit’s recommendation systems, designing models, training pipelines, and low-latency serving to improve personalization across the platform.
Director-level AI Architect at PwC's Data & Analytics Advisory practice in Bengaluru, designing ML pipelines, LLM serving/GPU infrastructure, and LLMOps solutions for clients using cloud platforms and frameworks like MLflow, DeepSpeed, and LangChain.
Principal-level ML architect at Adobe responsible for end-to-end technical coherence across large-scale distributed training systems, inference infrastructure, model architecture, and data pipelines for next-generation video and image foundation models (GenRender6/Gen6.5, GenEdit1).
Senior Machine Learning Engineer on the Applied Science Data Frameworks team building foundational infrastructure for large-scale multimodal AI training and inference, working with distributed systems, data engineering fundamentals, and ML infrastructure using technologies like PyTorch, Apache Ray, Spark, and Docker.
The AI Infrastructure Engineer will design and operate the platform layer for large-scale AI training and inference, focusing on GPU clusters, distributed frameworks, and developer tooling. The role requires extensive experience with high-performance computing, cloud infrastructure, and ML frameworks like PyTorch and Ray.
The Generative AI Engineer will design and operationalize fine-tuning workflows for large language models using Python, PyTorch, and various preference optimization techniques. This role involves managing training pipelines, evaluating model performance, and collaborating with cross-functional teams to deliver production-grade AI solutions.
GPU Systems Engineer - Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic…
The Machine Learning Research Engineer bridges applied research and production engineering by designing and deploying advanced machine learning systems. The role requires expertise in Python, PyTorch or JAX, and deep learning to solve business problems across natural language, vision, and recommendation domains.
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