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Senior ML Engineer to research and build AI models for physical systems like robots, combining reinforcement learning, multimodal models, and real-world robotics in a collaborative research environment.
Build cloud-native infrastructure and scalable pipelines to train and deploy large AI models for drug discovery and biomedical research using Python, PyTorch/TensorFlow, and AWS/GCP/Azure.
Architects and deploys scalable GenAI and ML systems for smart manufacturing, focusing on distributed training and robust pipelines in cloud/on-prem environments.
Principal engineer builds and owns the AI and data platform that turns scientific data into reusable assets for model training, evaluation, and automated workflows in a secure, scalable environment.
Principal engineer builds and sets direction for AI/data infrastructure that turns scientific data into reusable assets for model training and automated workflows in Python.
Architect and build scalable AI and data pipelines for scientific applications, focusing on clean, reusable datasets and end-to-end model training workflows using Python and cloud technologies.
Build and maintain ByteDance’s ML platform, handling data processing, model training, and deployment for large-scale deep learning systems powering ads and search.
Build and optimize distributed orchestration frameworks for large-scale ML training and inference in Kubernetes, focusing on resource efficiency and next-gen recommendation systems.
Develops and optimizes a Python-based machine learning framework for search, ads, and recommendation systems using TensorFlow/PyTorch.
We are dedicated to creating the world’s most advanced, reliable, and commercially scalable humanoid robots. Our flagship next‑gen labor automation units are designed to seamlessly integrate into daily life and amplify…
Research and develop core AI technologies, optimize deep learning models, and deploy large-scale systems while applying AI across industries like 5G/6G, healthcare, and finance.
Design and optimize large-scale AI models, build deep-learning architectures, and deploy end-to-end AI systems using TensorFlow/PyTorch and Python/C++.
Build and optimize production-grade LLM systems, integrating commercial APIs and self-hosted models, and implementing RAG pipelines and end-to-end LLM workflows.
Build and optimize distributed ML training/inference pipelines for low-latency trading systems using PyTorch, CUDA, and GPU acceleration.
Senior ML engineer builds multimodal medical imaging AI systems—foundation models, report generation, lesion detection—using Python/PyTorch and collaborates with clinicians to deploy in hospitals.
Build and deploy production-grade LLM chatbots and RAG pipelines using commercial APIs and self-hosted open-source models, optimizing for latency, cost, and reliability.
Lead training, alignment, and optimization of large language models using RLHF, SFT, and quantization; build reward models, red-team models, and optimize inference pipelines in Python/C++/Rust.
Build and scale the ML infrastructure that powers Nearmap’s aerial imagery AI products, including real-time model serving, distributed training, and LLM platforms on AWS/GCP.
Build and scale the ML platform that powers Nearmap’s aerial imagery analytics and generative AI products, running on AWS/GCP with Kubernetes, Ray Serve, and GPU workloads.
Build and scale the ML infrastructure that powers Nearmap’s aerial-imagery AI, including EKS batch inference, Ray Serve real-time serving, and GPU training on AWS/GCP.
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