Top AI Engineer Jobs

Build and ship agentic AI features across the full stack: design LLM-driven workflows, implement backend APIs and frontend UI, integrate MCP server capabilities, monitor production behavior, and iterate based on customer use. Drive projects independently, collaborate with product/design/engineering, and use agentic coding tools to prototype and deliver end-to-end features.

Lead development and optimization of a data‑intensive AI benchmarking stack: build Python-based backend systems and APIs, implement model evaluation and analyses, create interactive data visualizations, run custom enterprise analyses, and help evolve benchmarking methodology.

Design, build, and operate CI/CD, Kubernetes-based platforms, deployment automation, and observability for engineering workflows. Improve reliability, performance, and scalability across cloud and on-prem environments, debug cross-boundary failures, perform root-cause analysis, and deliver durable platform software and self-service tooling.

Execute hardware bring-up, validation, and telemetry monitoring for Cerebras AI clusters in data centers. Perform power-on sequencing, first-line troubleshooting, log collection, incident support under senior guidance, and contribute feedback to tooling and documentation while learning system architecture and networking fundamentals.

Design, implement, and maintain Python frameworks and services that orchestrate distributed engineering workflows across machines and clusters. Build scheduling, execution, resource management, failure recovery, and test infrastructure. Define APIs and abstractions, reason about concurrency and distributed-systems behavior, debug complex multi-system issues, write automated tests and documentation, and partner with platform, CI, release, QA, and product teams to deliver scalable infrastructure.

Integrate, validate, and productionize cross-stack inference features across AI frameworks, runtime, compiler, kernels, distributed systems, and hardware. Drive zero-to-one projects, debug system-wide failures, manage accelerated timelines, and improve automation, diagnostics, and repeatable integration practices while collaborating across software and hardware teams.

Lead design and execution of automated tests for large-scale AI clusters. Define test strategies, automate reliability, performance, stress, failure, and security tests across software (Kubernetes, monitoring) and hardware (wafer-scale accelerators, CPUs, interconnects). Debug distributed systems and datacenter hardware, and champion cluster uptime and observability.

Lead synthesis, placement, routing, and full-chip physical design for high-speed AI silicon. Perform block and full-chip physical verification, timing closure, power/performance/area optimization, DRC/LVS and IR/EM resolution, STA and constraint drafting, floorplanning, and script-based flow enhancements. Collaborate with RTL teams to integrate blocks into the full-chip architecture.

Architect and drive a unified, self-service inference control plane and reliability practices for large-scale multi-datacenter and cloud inference fleets. Build capacity orchestration, rollout safety, observability, SLO-based reliability, incident response, and automation; mentor senior SREs and measure impact through reduced toil, faster deployments, and SLO compliance.

Design, build, and maintain developer tooling for the MAX AI inference stack, including profilers, tracers, debuggers, dashboards, and agent-native tools. Integrate tooling across MAX, Mojo, and driver APIs, collaborate with kernel/model/serving engineers, and drive performance analysis, debugging, and observability to improve developer productivity and production reliability.

Build, test, and maintain AI pipelines and tools for the newsroom; integrate LLMs and ASR into workflows; train staff; establish ethical guardrails, monitor usage, and collaborate with product and editorial teams to improve journalism through AI.

Lead design, prototyping, deployment, and continuous improvement of AI-powered applications, agents, copilots, and automations. Integrate AI with portals, workflows, data platforms, and AWS; define reusable patterns (RAG, prompts, agent workflows); ensure secure, observable, and cost-effective production systems; measure adoption and ROI; and mentor engineers, contractors, and vendors.

Build and operate full‑stack features for an AI-driven contract management platform. Collaborate with product, data science, and design; own services from development to production; mentor teammates; apply DevOps, CI/CD, containerization, and distributed systems best practices to scale AI capabilities.

Build and scale production full-stack features using C#/.NET and modern frontends, integrate and manage agentic AI workflows with robust state management, ensure compliance and observability, manage AWS infrastructure with IaC, maintain CI/CD, mentor engineers, and drive testing and reliability in regulated environments.

Lead AI-assisted software engineering across the SDLC, design and implement large-scale C#/.NET microservices, deploy LLMs and ML models to production, modernize legacy systems to cloud-native architectures, work with containers, CI/CD and databases (MongoDB/Aerospike/Postgres), and mentor engineers while establishing AI-assisted development practices and documentation.

Design, train, optimize, and validate deep learning and computer vision algorithms for edge devices. Implement and optimize models for runtime on Ambarella hardware and assess performance on large-scale datasets.

Work as an early-career full-stack software engineer building and maintaining web application features across backend, frontend, APIs, data, tests, and internal tooling. Use AI-assisted development tools responsibly, write maintainable code and tests, participate in code reviews, and learn product and customer context to deliver production-quality outcomes.

Design, build, and own a multi-tenant enterprise SaaS backend that feeds ML data and BI. Collaborate with product, data science, UX, and users to deliver scalable, reliable customer-facing AI solutions. Drive systems design, database schemas, development lifecycle standards, technical docs, and code reviews while using AI/agentic tools to accelerate development.

Design and build AI-enabled solutions to accelerate analog circuit design, sizing, and layout. Develop and train discriminative and generative models (including LLMs and transformers), build data pipelines, test and monitor models, collaborate with analog design partners, participate in design reviews, and produce technical documentation for production-ready deployments.

Design and implement full-stack applications and AI agents for the GenAI platform, ensuring rapid development and deployment across critical industries. Collaborate with teams to deliver secure and reliable cloud-native systems.

Develop and maintain Python and Golang automation for security and incident response, build SOAR playbooks (Cortex XSOAR), integrate systems via RESTful APIs, support AI initiatives (RAG, MCP, Agent communications), participate in Agile sprints, conduct code reviews and testing, and produce technical documentation.

Lead design, development, testing, and deployment of cloud-enabled AI/ML systems (computer vision, sensor fusion, decision support) for maneuver, surveillance, and engagement missions. Evaluate model performance on operational datasets, optimize for robustness and cyber resilience, troubleshoot deployed systems, and produce technical documentation and reports for stakeholders.

Design, develop, test, and deploy fullstack software solutions. Collaborate with product, operations, AI engineers, and data scientists. Implement CI/CD, contribute to architecture, and maintain cloud-deployed services.

Build infrastructure and AI systems for Hardware Intelligence: agent infrastructure, retrieval and evaluation pipelines, and tooling to reason over large-scale time series, telemetry, and engineering datasets. Prototype and ship AI-powered workflows for anomaly investigation and root cause analysis, work directly with customers, and establish engineering patterns as the platform scales.

Design, build, and maintain scalable MLOps pipelines and data workflows to train, fine-tune, deploy, and monitor multilingual text and speech translation models. Prepare and optimize multilingual datasets, support model evaluation and benchmarking, and enable low-resource language training while collaborating with data scientists to integrate models into production.