Top Remote AI Engineer Jobs

Own and evolve the AWS-based platform powering product and AI workloads. Design IaC (Pulumi), build CI/CD pipelines, manage observability, respond to incidents, support LLM workflows (Bedrock, SageMaker), and partner with engineering and ML teams to improve reliability, scalability, and developer experience.

The Senior AI NodeJS Engineer develops AI Agentic solutions, collaborating with teams, managing tasks, ensuring quality code, and staying updated on AI technologies.

Architect and build an AI-native finance operating system for Kraken, focusing on automation, systems architecture, and regulatory compliance.

The AI Sales Engineer will build relationships with customers, lead product demonstrations, handle objections, and act as a domain expert in ML. Responsibilities include collaborating with sales and technical teams to create value propositions and developing educational materials about ML and MLOps.

Lead design, build, and production of agentic AI systems: architect agent workflows, memory, orchestration, and evaluation frameworks. Provide technical leadership, mentor engineers, and deliver reliable, observable AI features integrated with data platforms.

As a Forward Deployed Engineer, you will develop AI agents and automation workflows, collaborate with teams, and ensure secure, reliable solutions integrating with various systems.

Design, build, and iterate on fullstack systems for AI agents. Collaborate with teams to enhance UX and contribute to internal best practices while engaging in research related to AI technologies.

The Lead Infrastructure and Reliability Engineer will enhance GPU operations, define scalability strategies, and develop organizational strengths in a high-demand AI infrastructure setting.

Lead technical vision and roadmap for Pinterest's AI Platform. Architect petabyte-scale data orchestration, model training/fine-tuning, and high-performance inference for Generative AI and Recommender Systems. Drive cross-functional initiatives, influence architecture across the company, and cultivate an inclusive engineering culture focused on quality and ownership.

Develop and fine-tune vision-centric large language models and visual encoders, build evaluation benchmarks, collect and prepare visual training data (including for RLHF), prototype architectures for production VLMs, publish research, and mentor junior researchers within a small applied ML research team.

As an AI Platform Backend Engineer, you'll design and deploy backend systems utilizing AI for real-world applications, optimize performance, and mentor junior engineers while working directly with clients to address complex challenges.

As an AI Platform Engineer, you will design and develop AI-driven backend solutions, optimize performance, and collaborate with customers and teams.

Profile, benchmark, and optimize large-scale LLM training and inference across GPUs, CPUs, HCAs, and switches. Focus on collective communication and high-performance networking, build PyTorch trace-based profiling and replay tools, define performance test plans, and collaborate across hardware and software teams to identify bottlenecks and achieve performance targets.

As a Principal Software Engineer, you will lead AI networking system transformations, manage complex customer engagements, and influence NVIDIA's networking technologies.

The Senior Generative AI Engineer will design and deploy AI/ML solutions, focusing on Generative AI systems and their operational excellence across various sectors like R&D and Clinical Trials.

Lead development of AI-assisted engineering tooling and infrastructure so agents can autonomously run in cloud dev environments, access CI/CD and internal systems, execute tests, and improve developer productivity through integrations, documentation, and enablement.

Own and evolve Syndi, an agent runtime powering in-product chat and integrations. Design, ship, and operate core runtime surfaces (agent loop, tool execution, memory, context), build eval systems, ensure observability and reliability, review consumer integrations, and implement compliance and legal requirements for production LLM systems.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.

Build and maintain production-grade React+TypeScript web interfaces for data-intensive, real-time AI products. Own end-to-end frontend features, optimize UI performance for large datasets, collaborate with design and backend teams, establish frontend best practices, improve architecture and tooling, and resolve production issues across browsers and devices.

Owner/implementer of end-to-end full stack features for data-intensive AI products: design and build scalable Node.js backends, REST APIs, and React+TypeScript frontends; optimize performance; establish testing, observability, and CI/CD; collaborate with product, design, and ML teams; debug and support production systems.

Build and maintain full-stack product features, backend services, APIs, data pipelines, and AI integrations to transform customer interaction data into AI-powered experiences. Collaborate with cross-functional teams to design, test, and support production systems, internal tools, and user-facing interfaces. Own ambiguous problems end-to-end and raise engineering quality across the data and AI platform.

Embedded with strategic customers, own end-to-end design, build, and production rollout of cloud infrastructure for large-scale GPU/HPC AI workloads. Build compute orchestration, platform services, onboarding sandboxes, and reliability/security tooling. Turn field learnings into reusable platform capabilities and partner with Product and Engineering to productize them.

Design, develop, and maintain microservices-based, AI-enabled software for safety-critical air-traffic systems. Work in Agile/SAFe teams to implement CI/CD/DevSecOps practices, integrate with databases, messaging, and external APIs, use AI-assisted tools, and support testing, performance tuning, and reliable delivery while meeting Public Trust and U.S. citizenship requirements.

Design, implement, and operate a private, secure AI platform and autonomous agent workforce for engineering workflows. Build agents (FastAPI/CLI/chat), integrate MCP and RAG, maintain containerized infrastructure, CI/CD validation, secrets and cost management, and enterprise integrations (GitHub, Jira, Confluence, Teams). Continuously evaluate models, improve agent reliability, and drive platform adoption across low-level systems engineering domains.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.