AI Systems Engineer Jobs

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Build core primitives end to end including entity ownership, audit, authorization, and orchestration, ensuring the right actions are the default and incorrect actions are difficult. Own the domain model by turning Fluidstack's concepts of power, datacenters, and chips into composable entities that remain durable over time. Define interactions with external systems by interfacing with vendor systems and ingesting domain-specific formats such as KMZ, BIM, Revit, and vendor documents. Enable AI agents to be first-class operators of Fluidstack's systems by providing tools, guardrails, and audit trails to allow safe and effective operation beyond mere advice.

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Build and scale reinforcement learning environments and platforms behind them; drive architecture for scalable, reliable, extensible environment systems and data generation pipelines; partner with Research, Product, and Ops teams to turn ambiguous needs into production systems; build modular, plug-and-play domains that integrate cleanly with training and evaluation loops; improve reliability, observability, performance, and data quality of systems.

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The Host Systems Software Engineer is responsible for designing, implementing, and debugging host-side systems software for AI infrastructure, including Linux kernel drivers and supporting userspace components. They build and optimize software paths for high-throughput, low-latency communication such as RDMA and related networking functionality, and develop software related to PCIe, DMA, NICs, accelerators, memory movement, and device interaction. The role involves bringing up new hardware platforms, diagnosing complex issues across kernel, firmware, networking, and hardware boundaries, and building tooling for integration, testing, diagnostics, observability, qualification, and performance characterization. Collaboration with hardware, networking, and platform teams to define interfaces and integrate new capabilities is essential, as is working with external vendors to integrate technologies and resolve issues. The engineer contributes across the systems software stack as the platform and team evolve and helps shape the technical direction and engineering practices for the growing systems software stack.

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Design and build extensible backend systems that support flexible configurations for different customers and content types. Develop infrastructure that interfaces cleanly with large language models (LLMs), enabling prompt engineering, context injection, and modular evaluation workflows. Build tooling and platforms that enable fast iteration by AI engineers and analysts, including declarative pipelines, parameterized jobs, and reproducible experiments. Prioritize ease of deployment, integration, and testing for both internal teams and external partners. Collaborate closely with product, data, and policy teams to translate nuanced safety needs into scalable, maintainable software systems.

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Design, implement, and operate cutting-edge ML Training OR Inference performance optimization techniques to scale VLM, VLA, and Foundational models and deploy them efficiently in robotaxis. Collaborate closely with cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.

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Contribute to the C++ runtime that powers LM Studio. Extend LLM engine integrations and build platform-aware performance features for desktop operating systems. Implement resilient IPC, resource management, and scheduling logic to support concurrent model execution. Improve build, packaging, and release infrastructure for native components. Collaborate with the team to deliver cohesive and recognizable user experiences.

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As a Senior Staff Software Engineer at Ironclad, you will serve as the Technical Lead Architect and owner of Ironclad’s document and record service, driving the technical strategy and decisions to ensure reliability, scalability, and performance. You will partner across engineering teams to ensure the document service unlocks new feature capabilities and AI innovation. You will mentor and provide thoughtful feedback to other engineers and cultivate a culture of engineering excellence. Additionally, you will collaborate cross-functionally with product managers, designers, and other engineers to spec, design, and build features and systems. You will partner with quality engineers to define, build, and maintain testing strategies. You will also participate in interviewing and recruiting talented engineers to Ironclad. Your work will involve building on a modern technology stack including TypeScript, Node.js, PostgreSQL, Temporal, and Docker containers in Kubernetes on Google Cloud Platform, and applying AI tools and features to power the future of contract intelligence.

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Design and implement core product features in close collaboration with the founding team. Find pragmatic solutions to complex algorithmic problems. Strike a balance between scalable code design and development speed, with an emphasis on shipping fast. Contribute to shipping new features and improvements to the products. Scale the existing software stack and algorithms to serve the most demanding use-cases. Support the team in establishing a stable, high quality and flexible software engineering process.

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Software Engineers at Cognition are responsible for building core agent infrastructure including design and shipping systems that power Devin's long-horizon task execution such as tool use, context management, multi-step planning, subagent orchestration, and sandboxed code execution environments. They contribute to improving Windsurf as an AI-native IDE by enhancing editor intelligence, agent-in-the-loop workflows, real-time code understanding, and the developer experience. Their role includes working directly with researchers to translate new model capabilities into shipped features, owning reliability and performance at scale by building systems that handle millions of agentic tasks with low latency, high reliability, and correctness for production use. They also have input into the future direction of AI software engineering at Cognition.

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Contributing to the development of Lumana's core edge platform designed to run across diverse hardware environments as part of a hybrid edge-cloud architecture. Developing and integrating video management capabilities including streaming, recording, and real-time AI processing. Designing and optimizing systems for low-latency, high-performance workloads at the edge. Building secure and efficient communication pipelines between edge devices and cloud systems. Working closely with AI/ML engineers to deploy and optimize models on-device. Participating in code reviews, debugging, and performance tuning across the stack. Collaborating with a team of engineers, AI experts, and leadership in a fast-moving environment.