AI Platform Engineer Jobs

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Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.

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Set technical direction for the data platform by owning the architecture roadmap for Fiddler's ingestion, storage, and query layers. Drive multi-quarter initiatives from problem framing through design, implementation, and rollout. Design systems for 10x scale by leading the evolution of the ClickHouse-backed analytics layer and Kafka-based ingestion pipeline to handle significant growth in event volume, query complexity, and tenant count. Define the event model for next-generation AI workloads by architecting the data model and storage strategy for agentic application traces, LLM evaluation pipelines, and enrichment workflows, balancing flexibility, query performance, and schema evolution. Drive cross-team technical decisions by partnering with Backend, Monitoring, and Enrichment teams to ensure platform abstractions meet their needs and represent the Platform perspective in company-wide architecture reviews. Own platform reliability and cost efficiency by establishing SLOs, capacity planning processes, and cost optimization strategies for data infrastructure, and making build-vs-buy decisions for infrastructure components. Raise the engineering bar by mentoring senior engineers and establishing patterns and guardrails including data modeling conventions, query optimization practices, and testing strategies that have team-wide impact. Lead by example in code review, design documentation, and incident response. Influence product direction by working with Product and Customer Engineering to translate customer data challenges into platform capabilities and help define priorities and risks for future work.

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As a core backend engineer at Brain Co., you will design, build, and operate the platform backend services and data pipelines that power Brain Co.'s AI products. You will own the full lifecycle from initial architecture and implementation to deployment and long-term maintenance. You will build critical systems that accelerate AI product development including designing scalable solutions for ML experiment tracking, artifact management, and automated training and evaluation pipelines. You will engineer highly available, fault-tolerant systems with deep observability that meet strict uptime and latency SLAs demanded by enterprise and government clients. You will design modular and scalable architectures and clean APIs (REST, gRPC) with a long-term platform mindset and continuously profile systems to optimize latency, throughput, and cloud compute costs. You will act as the bridge between engineering, product, and ML research teams to build shared platform capabilities to remove bottlenecks and reduce the time required to ship new AI products.

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Collaborate with AI research scientists to understand and implement state-of-the-art AI algorithms. Develop and maintain software that incorporates AI technologies into practical applications and products. Ensure the scalability, efficiency, and reliability of software systems. Participate in the entire software development lifecycle, including requirement analysis, design, coding, testing, reviewing, deployment, and support. Write clean, efficient, and well-documented code. Stay updated with the latest trends in software engineering and AI technologies. Support the development of a user facing AI product. Work cross-functionally with product managers, designers, and other engineering teams to deliver high-quality products.

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Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, P&R, STA, signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and quality of results (QoR). Optimize EDA tools and custom CAD flows using data-driven and ML-based techniques, in close collaboration with verification, extraction, timing, DFT, and EDA vendors.

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Design, build, and maintain foundational frameworks and tools to empower expert teams to experiment fast and turn ideas into production-ready systems. Collaborate with expert teams to validate use cases and build robust solutions, aiming for modular and reusable components. Identify and mitigate high-level code design flaws and development workflow inefficiencies that cause friction and hinder productivity across the organization. Advocate for good practices and maintain high code quality through code reviews, documentation, and training.

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As a Staff Software Engineer on the Core Infrastructure team at Harvey, your responsibilities include designing and building scalable, fault-tolerant infrastructure systems that power Harvey's AI platform across multiple cloud regions. You will own and evolve the multi-cloud infrastructure (Azure, GCP), including Kubernetes orchestration, networking, and container management. You will lead technical initiatives focused on observability, incident response, and operational excellence, building systems for rapid detection and resolution of issues. Architecting and optimizing distributed systems for reliability, including load balancing, quota management, and failover mechanisms, will be part of your role. You will partner with Product Engineering and Security teams to ensure infrastructure accelerates product development, drive infrastructure-as-code practices using tools like Terraform and Pulumi for reproducible deployments, and mentor engineers through code reviews, design reviews, and technical leadership. Representative projects include designing model proxy architecture for handling inference requests, building distributed rate limiting and quota management systems, architecting multi-region deployment strategies for data residency compliance, developing observability infrastructure with SLA monitoring and cost tracking, and leading CI/CD pipeline evolution to improve velocity and stability.

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Develop systems and tooling to measure, monitor, and improve token throughput across first-party and partner-owned compute environments. Support performance benchmarking, tokenomics analysis, and model porting across heterogeneous infrastructure environments. Build tooling to integrate external or partner infrastructure into OpenAI’s internal compute, observability, and workload management systems. Develop and monitor operational metrics including billing, usage, SLAs, utilization, reliability, and throughput. Identify bottlenecks across hardware, networking, software, and workload enablement that prevent capacity from becoming productive tokens. Partner with compute, infrastructure, networking, finance, and operations teams to translate raw capacity into usable workload-serving capacity. Build dashboards, automation, and reporting systems that provide clear visibility into TaaS capacity, performance, and business outcomes.

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As a Software Engineer at Mirage, you will work across product engineering, backend/platform engineering, and applied AI teams. Responsibilities include designing and building systems, APIs, and infrastructure that power products; solving challenges involving distributed systems, scaling, and performance; integrating and operating large AI models in production; building core platform components such as storage, billing, observability, and security; shipping end-to-end product experiences for creative workflows; building polished, performant user interfaces (web or native mobile); pushing the boundaries of video, graphics, and AI-powered creation tools; instrumenting, A/B testing, and iterating quickly with real user data; building and shipping AI-powered product experiences end-to-end; working with state-of-the-art models across video, audio, image, and text; designing systems for context, reasoning, and intelligent behavior; and building evals, datasets, and tooling for improving model quality.

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Define and drive architecture for scalable, extensible Reinforcement Learning Environments (RLE) systems and data pipelines. Lead development of platform capabilities enabling rapid domain creation. Partner with Research, Product, and Operations to shape strategy and execution. Set standards for reliability, observability, performance, and data quality. Mentor engineers and elevate engineering excellence across the team. Identify and solve systemic bottlenecks in scaling environments and data generation.