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Cloud Engineer - AI ML
Provide Level-3 support for Google Cloud’s AI/ML services (Vertex AI, GenAI, Conversational AI), diagnosing and resolving complex customer incidents while maintaining SLOs and documentation standards.
AI Software Development Engineer
Develop and optimize deep learning frameworks (PyTorch, TensorFlow, SGLang) for AMD GPUs, focusing on GPU kernel performance, LLM inference, and distributed training across multi-GPU and multi-node systems.
Machine Learning Engineer - ML Training Platform
Build and optimize a distributed ML training substrate that trains large models across many low-bandwidth nodes using model parallelism and P2P networking.
Deep Learning Engineer - Credit
Build and deploy deep learning models for credit risk, transitioning legacy systems to modern architectures and applying reinforcement learning for dynamic pricing and limit decisions.
Lead Machine Learning Engineer (Foundation Models)
Lead the development of Grab’s proprietary foundation models and generative recommendation systems, scaling distributed training and deploying AI solutions for millions of users.
AI Engineer (AI Products)
Build, train, and deploy multilingual LLMs for underrepresented languages, focusing on data pipelines, distributed training, and real-world AI applications.
Deep Learning Engineer - Credit
Design and deploy deep learning models for credit risk, replacing tree-based systems with large-scale pre-trained architectures, reinforcement learning for pricing, and embedding-based feature extraction.
Binance Accelerator Program - LLM Recommendation & Agentic AI Engineer
Build LLM-powered recommendation systems and agentic AI for crypto trading and Web3 use cases using PyTorch, RAG, and reinforcement learning.
Senior Associate, Generative AI, Data and Analytics, Advisory
Design and optimize ML pipelines, fine-tune LLMs, and deploy scalable LLM services using frameworks like MLflow, vLLM, and Kubernetes.
Senior Associate, Generative AI, Data and Analytics, Advisory
Design and deploy ML pipelines, fine-tune LLMs, and orchestrate GPU-based serving for enterprise GenAI solutions using frameworks like PyTorch, LangChain, and Kubernetes.
Senior Machine Learning Engineer
Build and scale ML systems for ad ranking, bid optimization, and real-time recommendations using PyTorch/TensorFlow to improve CTR, CVR, and ROI in a mobile advertising platform.
AI Research Engineer
Design and deploy advanced machine learning systems, bridging research and production to solve business problems using deep learning, LLMs, and scalable ML pipelines.
GPU Software Engineer (CUDA)
Build and optimize CUDA kernels for AI/HPC workloads, tuning GPU memory and compute to maximize throughput in production systems.
LLM Engineer
Designs and runs fine-tuning workflows for large language models using supervised, DPO, RLHF, and related techniques, while building scalable training pipelines and rigorous evaluation suites.
AI/ML Engineer, Senior
Senior AI/ML Engineer building and deploying NLP, LLM, and computer-vision models for national-security missions, using Python/Java/Scala/Rust and MLOps pipelines.
ML Infrastructure Engineer
Design and operate GPU clusters, distributed training frameworks, and scheduling systems to power large-scale AI workloads with a focus on reliability, efficiency, and cost control.
Learning Experience Designer
Designs and builds scalable learning content (eLearning, videos, job aids) for Blue Origin employees using authoring tools and AI-assisted workflows.
Machine Learning Engineer, AI Inference Solutions (University Grad)
Build and optimize ML deployment platforms and inference pipelines for autonomous-vehicle software, shipping PyTorch models to GM’s Super Cruise fleet with real-time latency and safety constraints.
Amazon Software Engineer- AI/ML, Amazon Neuron Training
Build and optimize Neuron, AWS’s ML compiler/runtime for training GenAI models on Trainium chips, tuning parallelism and kernels across PyTorch/JAX to maximize throughput.
Senior Machine Learning Infrastructure Engineer, Embedding Platform
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.