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Build and scale SpaceXAI’s high-throughput API in Rust/C++ to serve AI models globally with low latency, handling billions of tokens per minute.
Build and optimize backend services that ingest, index, and serve complex financial data for AI-powered workflows using PostgreSQL, ClickHouse, Kafka, and ElasticSearch.
Principal Data Engineer builds and leads the AI data stack for Anaplan’s LLM and agentic systems, designing retrieval layers, vector/graph databases, and real-time GenAI features for enterprise planning workflows.
Principal Data Engineer builds and leads AI systems at Anaplan, designing retrieval layers, RAG pipelines, and GenAI features that integrate LLMs into real-time planning workflows.
Overview Stats Perform is the market leader in sports tech. We provide the most trusted sports data to some of the world's biggest organizations, across sports, media, and broadcasting. Through the latest AI…
Build and scale SentinelOne’s AI Gateway (Kong AI Gateway) to route, secure, and monitor AI coding assistant traffic, while operating self-hosted LLM inference stacks and LLMOps tooling across Kubernetes.
Build and scale SentinelOne’s AI Gateway (Kong AI Gateway) to route, rate-limit, and monitor AI coding assistant traffic, while operating self-hosted LLM inference stacks and LLMOps tooling across Kubernetes and AWS.
Build and own the shared AI platform that trains and serves Adobe’s generative AI models at global scale, focusing on GPU fleet utilization, low-latency inference, and end-to-end model deployment pipelines.
Develops and optimizes inference software for large language models using TensorRT-LLM, focusing on performance and scalability across platforms.
Design and deploy a secure, on-premises AI platform in a classified environment, building scalable HPC infrastructure and DevSecOps ecosystems for mission-critical defense programs.
Design and implement AI/ML solutions—including LLM-powered workflows and agent-based automation—to streamline classified-data analysis and support rapid decision-making for national security missions.
Design and scale Kubernetes-native environments for distributed robotics AI workloads, including simulation, synthetic data generation, and inference using NVIDIA frameworks like OSMO and Isaac Sim.
Design and scale an enterprise AI platform using Red Hat OpenShift and OpenShift AI, focusing on container-native MLOps, LLM serving, and automated benchmarking for production deployments.
Builds and scales ML infrastructure for post-training large language models, focusing on RL environments, compute scheduling, and research tooling to accelerate AI experimentation.
Build reinforcement-learning environments and reward functions to train frontier AI models, blending research with engineering in a startup setting.
Build and optimize low-level compute kernels and inference pipelines for large language models running on custom ML hardware, integrating with frameworks like PyTorch and vLLM.
Builds and optimizes low-level compute kernels and serving infrastructure for large language model inference on custom ML hardware, integrating frameworks like PyTorch and vLLM.
Build and scale foundational large language models for Amazon’s shopping experiences, focusing on ML infrastructure, post-training, and reinforcement learning to improve personalization and customer interactions.
Build and optimize low-level compute kernels and serving integrations for large language model inference on custom ML hardware, spanning model execution, memory management, and distributed systems.
Build and deploy LLM- and SLM-powered agentic systems for healthcare decisioning, including clinical reasoning, prior authorization, and claims integrity, using frameworks like LangGraph and LangChain.
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