AI Software Engineer Jobs

View detail

As the Senior Director of Engineering for Developer Experience at Crusoe, you will own and drive the strategy, execution, and culture of the team responsible for how Crusoe's engineers and non-engineers build, ship, and operate software. Responsibilities include defining and executing the long-term vision for Crusoe's internal developer platform, which encompasses shared services, internal APIs, repositories, and self-service infrastructure to enable engineering teams to move quickly and confidently. You will also rapidly develop and productionize AI-powered tools for the entire company, creating and evangelizing best practices for productionizing AI-developed tools and evaluating SaaS purchases. Additionally, you will oversee the design, reliability, and continuous improvement of CI/CD pipelines, build systems, and deployment infrastructure to ensure safe and rapid scaling of engineering teams' shipping processes. Your role will also involve defining and driving organization-wide engineering productivity initiatives by establishing metrics, identifying bottlenecks, and implementing tooling and process improvements that enhance developer experience across Crusoe. People leadership is a key responsibility, including managing and growing a team of engineers and fostering a high-performance culture based on accountability, innovation, and continuous learning. Furthermore, you will collaborate with senior leaders across Engineering, Infrastructure, Security, and Product to align Developer Experience investments with company-wide engineering goals and priorities.

View detail

As the Engineering Manager on the Autonomous team, you will lead and scale a high-calibre team of engineers dedicated to defining the future of AI agent development and advancing AI and backend systems. You will oversee the technical roadmap for the Autonomous team, translating architectural complexity into clear product strategies. You will mentor a diverse group of engineers, supporting their professional growth. You will partner closely with Product and Design to ensure the agent-building tools remain intuitive while supporting technical capabilities. You will champion a 'show > tell' culture by ensuring rapid shipping with a high standard for technical stability and user experience. You will clear technical and operational roadblocks to ensure the team operates with high agency and clarity.

View detail

As an Engineering Manager on the Autonomous team, you will lead and scale a high-calibre team of engineers dedicated to defining the future of AI agent development and advancing AI and backend systems. You will oversee the technical roadmap for the team by translating architectural complexity into clear product strategies, mentor and support the professional growth of a diverse group of engineers, and partner closely with Product and Design to ensure the agent-building tools remain intuitive and technically robust. You will champion a "show > tell" culture to ensure rapid shipping while maintaining high technical stability and user experience standards, and clear technical and operational roadblocks to enable the team to operate with high agency and clarity. You will act as the bridge between product vision and technical execution.

View detail

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.

View detail

The Software Engineer for the Inference Platform at Fluidstack will own inference deployments end-to-end, including initial configuration, performance tuning, production SLA maintenance, and incident response. They will drive measurable improvements in throughput, time-to-first-token (TTFT), and cost-per-token across diverse model families and customer workload patterns. Responsibilities include building and operating key-value (KV) cache and scheduling infrastructure to maximize utilization across concurrent requests, implementing and validating disaggregated prefill/decode pipelines, and managing Kubernetes-based orchestration at scale. The role requires profiling and resolving bottlenecks at compute, memory, and communication layers, instrumenting deployments for end-to-end observability, partnering with customers to translate model architectures, access patterns, and latency requirements into deployment configurations, and contributing to the inference platform architecture and roadmap focused on reducing deployment complexity, improving hardware utilization, and expanding support for new model classes and accelerators. Additionally, participation in an on-call rotation (up to one week per month) to maintain reliability and SLA commitments of production deployments is required.

View detail

The Senior Software Engineer will build a powerful project innovating customer support by defining what an AI-first SaaS product looks like, addressing unique UI/UX, capabilities, and data model challenges of an AI-first company. They will lead ambitious and ambiguous projects involving strong technical decision-making, effective implementation, and incorporate product and design instincts. The engineer will work across the tech stack, collaborate with a top-caliber team, and mentor or lead less experienced engineers. They will participate in an engineering-led culture where everyone owns working with users and building a great product, taking ownership of challenging problems and defining and implementing solutions.

View detail

Design a collaborative "Multiplayer Computer" that lets humans and AI agents work together on shared shells, filesystems, and state—conflict-free and in real time; build high-throughput backend applications and services; create tooling that helps AI systems minimize mistakes through static analysis and deterministic techniques; develop infrastructure (frontend & backend) that empowers product engineers to rapidly ship delightful user experiences; support sophisticated user interfaces, including terminals, code editors, window-management systems, and innovative experiences that require both creativity and algorithmic skill; and bridge the gap between prompt engineers and frontend engineers. Telecommuting is permitted with in-office presence required three times a week (Monday, Wednesday, Friday), with only incidental domestic travel required.

View detail

Design and build GenAI systems that turn large language models (LLMs) into composable, dependable tools, leveraging retrieval, tool use, agentic reasoning, and structured outputs. Collaborate with ML and infrastructure engineers to scale and optimize GenAI workflows, manage latency, context windows, and model choice. Write high-quality, modular code that handles failure gracefully, is flexible to change, and easy to iterate on. Own major architectural decisions regarding workflow architecture, data flow, caching, and structuring generative outputs. Drive rigorous evaluation by building benchmark datasets, developing automated and human-in-the-loop evaluation frameworks, designing experiments to identify failure modes and edge cases, conducting A/B tests to inform deployment, and using clinician feedback to guide model improvement. Prototype rapidly with new models, open-source tools, and novel prompting techniques. Own the end-to-end productionization of LLM workflows: deploy models in low-latency, high-uptime environments, build monitoring and observability systems, implement post-processing guardrails, and manage workflow versioning.

View detail

As a Training Runtime: Process Management Engineer, you will design, build, and maintain software to orchestrate and monitor machine learning workloads on large supercomputers, working primarily with Python and Rust. Your responsibilities include profiling and optimizing the software stack to support computation orchestration at frontier scale, improving reliability, observability, and fault tolerance for long-running jobs, debugging complex distributed systems issues across large clusters, and responding to the changing shapes and needs of the ML systems to enable researchers. The role involves building high-performance asynchronous systems with a strong emphasis on performance, correctness, and scalability, and working on software that ties thousands of computers together as a unified system while promoting a fast debugging and development cycle and relentless optimization for scale, stability, and performance.

View detail

The responsibilities of the systems research internship include investigating hard systems problems at the intersection of systems engineering and research, building meaningful systems or prototypes, and carefully measuring their impact to improve Applied Systems' efficiency, scalability, and reliability. Typical focus areas are distributed systems and storage, compute and scheduling, performance engineering, reliability and observability, networking and data pipelines, and systems for machine learning. Internship projects may involve defining hypotheses, instrumenting existing production systems to gather metrics and analyze them, building or modifying real systems, conducting experiments and benchmarks, analyzing results, clearly communicating tradeoffs and recommendations, and publishing research in technical journals and conferences.