AI Software Engineer Jobs

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Design and build intuitive web interfaces for robot data annotation, datasets visualization, and experiment tracking. Utilize data-driven techniques to optimize interfaces for efficiency and fast iteration cycles. Integrate AI models to automate manual tasks. Work together with AI researchers, robot operators, and annotators to support new user experiences.

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The Senior Systems Performance Engineer at Crusoe is responsible for leading the evaluation and establishment of New Product Introduction (NPI) across varied hardware architectures with a focus on Bare Metal and VM environments. They conduct deep-dive performance evaluations and workload characterizations across compute, memory, storage, and networking. They develop sophisticated multi-variable projection models and frameworks to analyze system design options through tradeoffs such as Power and Total Cost of Ownership (TCO). The role involves collaborating with external vendors to drive platform customization and optimize server and AI architectures for maximum performance-per-TCO. They design and implement performance methodologies to scale evaluation processes for large-scale GPU/AI data centers. Additionally, they engage in industry research and contribute technical insights to consortiums and standards committees to influence future hardware roadmaps.

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Build and maintain features for the web-based property management platform using TypeScript, React, Node.js, PostgreSQL, and AWS. Contribute to a monorepo architecture, working within two-week sprint cycles to deliver high-quality code. Implement integrations including DocuSign, Plaid, Stripe, and ownership group payout systems. Optimize platform performance and user experience by replacing legacy systems. Build and integrate AI agents using Claude and other AI APIs to automate organizational processes, developing API integrations and custom agents. Collaborate with the CEO on prioritizing automation opportunities. Take ownership of tasks, independently research and implement solutions to challenges, proactively identify and implement improvements, and contribute ideas to platform architecture and development priorities.

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Design and build AI agents that outperform human agents in managing complex customer interactions and driving customer retention. Identify cross-customer trends that guide the evolution of Decagon’s agent building platform and research efforts. Experiment with and run evaluations on the latest text and voice models, then integrate them at scale with large enterprise-grade customers.

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Design and build AI agents that outperform human agents in managing complex customer interactions and driving customer retention. Identify cross-customer trends that guide the evolution of Decagon’s agent building platform and research efforts. Experiment with and run evaluations on the latest text and voice models, then integrate them at scale with large enterprise-grade customers. Have complete ownership and autonomy in building and shipping best-in-class AI agents, from initial implementation through continuous iteration, working directly with leaders across industries like finance, healthcare, and hospitality to solve their users’ needs with reliable and intuitive AI agents. Dive deep into complex system challenges and build elegant solutions that scale to millions of users.

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As a Product Engineer, you will dream up, build, and ship LM Studio features to millions of users worldwide at a fast pace. Your work will intermingle UI development with systems engineering, design, and applied AI/agentic engineering. You are expected to have a holistic understanding of software systems and the ability to work across the stack.

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Design, build, and optimize the core native runtime powering LM Studio and the C++ libraries powering the app and APIs. Work across runtime, LLM engines, llama.cpp/MLX integrations, build infrastructure, and on-device AI software. Focus on system and library integration by wiring the C++ runtime to GPU backends, vendor SDKs, and operating-system services to support user-facing applications. Implement and harden system-level code involving threading, memory, files, IPC, and scheduling. Integrate platform acceleration paths such as Metal, CUDA, and Vulkan across macOS, Windows, and Linux. Profile, debug, and tune execution paths to ensure fast, dependable local AI and maintainable software. Contribute to the C++ runtime powering LM Studio, extend LLM engine integrations, and build platform-aware performance features for desktop OS. 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 an Engineering Leader at Ema, you will build and lead a high-performance engineering organization by recruiting, hiring, and developing senior engineers across multiple sub-teams including cloud infrastructure, data platform, ML operations, and developer experience. You will establish engineering standards, a code review culture, on-call expectations, and promote a bias-toward-shipping mentality balanced with production rigor. You will coach and grow senior and staff engineers into technical leaders and manage engineering managers as the organization scales. Your responsibilities include setting the 6–18 month platform roadmap in partnership with engineering teams, making critical architectural decisions such as build versus buy and migration strategies, and driving cross-functional alignment with product, ML/AI research, and go-to-market teams. You will own production health for all platform services, including incident response, postmortems, SLO tracking, and capacity planning. Additionally, you will establish and refine engineering practices to maintain fast shipping without compromising reliability, and participate in executive-level reviews related to infrastructure spend, system health, and engineering velocity.

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Create functional black box tests for large codebases in various source languages, create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms, monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards, and leverage LLMs such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies, oversee the end-to-end health of the platform ensuring seamless integration between AI core and full-stack components, build automated systems to monitor model performance and data drift across geographically dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead response for production issues in mission-critical environments ensuring rapid resolution and prevention, translate technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to ensure field lessons influence future technical architecture and decisions.