Senior Software Engineer in Motorola Solutions' camera systems group, designing and optimizing modern C++ for embedded systems and deploying AI/computer vision models on camera hardware for real-time safety and security video analysis.
A two-year graduate programme in Bristol where you’ll build software connecting Graphcore’s ML accelerators with PyTorch and Triton, implementing features, optimising performance, fixing defects, and writing tests and documentation while learning about compilers and runtime software for AI workloads.
A graduate software engineer tests AI and machine learning workloads on Graphcore accelerator hardware, creating ML workloads, analysing numerical accuracy and performance, designing tests, and building CI/CD pipelines using Python, C++, Linux, and ML frameworks such as PyTorch, JAX, Triton, or TensorFlow.
Senior Solutions Architect bridging NVIDIA's GPU/AI platforms with industrial robotics customers — designing solutions using Isaac Sim, deep learning frameworks, and GPU-accelerated computing for manufacturing, humanoids, and edge AI deployments.
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping…
Designs, builds, and deploys production-scale AI solutions (agents, generative AI, ML) at Lumen, partnering with product and engineering to deliver measurable business impact using Python, PyTorch/TensorFlow, MLOps/LLMOps, and cloud AI platforms.
Designs and optimizes high-performance CUDA kernels and GPU libraries for AI training, inference, and HPC workloads. Works with ML engineers to profile production GPU systems using Nsight and NCCL, shipping performance improvements in C++/CUDA, PyTorch, JAX, or Triton.
Designs, builds, and operates the GPU/accelerator platform layer for large-scale AI training and inference, including scheduling, storage, networking, and developer tooling for ML engineers. Uses Python, Go/C++, Kubernetes, Slurm/Ray, and cloud ML infrastructure; 100% remote U.S. role.
This position does not provide sponsorship for employment now, or in the future, in any format. Position Summary: The Applied Artificial Intelligence Engineer is part of the Education Design and Informatics team within…
Member of Technical Staff for Model Training infrastructure and platform at Xperteez Technology. The role involves architecting and scaling distributed systems for supervised fine-tuning, reinforcement learning, and distillation across GPU clusters, owning inference optimization, experiment tracking, and platform architecture for AI research teams, using Python and systems languages such as C++, R
The engineer trains, fine-tunes, and deploys specialized language, vision-language, and robotics models for micro1's applications, owning the full model development lifecycle from dataset design to production inference optimization using Python and PyTorch.
Builds and scales distributed GPU infrastructure for large-scale model training (SFT, RL, distillation) and agent execution platforms, optimizing PyTorch/DeepSpeed/Ray pipelines, inference engines (vLLM, TensorRT-LLM), and Kubernetes-orchestrated clusters. Owns architecture from distributed training to experiment tracking and fault recovery.
Researches and scales advanced post-training methods (SFT, RL, distillation) for large language and multimodal models, designing experiments to improve reasoning, generalization, and agent capabilities using Python and PyTorch.
Staff Machine Learning Engineer at Cresta building production-grade agentic AI systems for contact centers, including LLM-powered agent augmentation, evaluation frameworks, and RAG pipelines. Requires 7+ years ML production experience, deep LLM expertise, and leadership in architecting complex AI systems.
The Machine Learning Engineer owns the full AI stack for an AI voice-sensing wearable, expanding a speech emotion recognition model into a physics-based harmonic engine and managing a dual-instance vLLM setup on GCP with Llama 3.1 8B and Qwen 2.5 32B, using PyTorch, Python, and audio ML frameworks.
Leads and scales an engineering team building NVIDIA’s deep learning inference software (vLLM, SGLang, FlashInfer) for GPU-accelerated AI model deployment. Drives roadmap, performance optimization, and cross-team collaboration for LLMs and generative AI on NVIDIA GPUs using CUDA, Triton, and NCCL.
LLM Engineer designing and executing fine-tuning workflows for large language models using supervised, DPO, and RLHF techniques, building scalable training pipelines on GPU clusters, and leading dataset construction and evaluation.
Designs and scales NVIDIA's automated inference and deployment platform for generative AI models such as LLMs and diffusion models. The role optimizes inference efficiency using PyTorch and HuggingFace model graphs, high-performance CUDA/TRT-LLM/Triton kernels, and techniques like model sharding and KV-caching within the TRT Model Optimizer stack.
Owns end-to-end computer vision projects for power-grid infrastructure, taking ML research from paper to production-deployed models for defect detection, thermal-anomaly identification, and vegetation monitoring. Day-to-day work uses Python, PyTorch/Lightning, OpenCV, and ML DevOps tooling within a remote U.S. team.
The Machine Learning Engineer trains, tunes, and evaluates large language models for healthcare use cases using techniques such as SFT, PEFT, DPO, PPO, and RAG, collaborating with engineers, data scientists, and medical professionals. Core technologies include PyTorch, Axolotl, vLLM, TGI, and llama-cpp.
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