Top Remote AI Engineer Jobs

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Build and deploy internal AI tooling, agents, integrations, and workflows that improve productivity across engineering, research, and business teams. Evaluate emerging AI tools and models, develop observability and responsible-use guardrails, and partner with stakeholders to prototype and scale solutions. Drive adoption through documentation, enablement, and hands-on support while integrating AI into autonomy, simulation, ML training, and business workflows.

Design and optimize AI runtimes across CPU and GPU hardware, improve performance through cross-stack optimization, port runtimes to new platforms, and develop benchmarking and performance-analysis systems. Collaborate with compiler, kernel, serving, model, infrastructure, tooling, and customer-success teams to support diverse AI workloads and customer requirements.

Build and deploy AI agents, intelligent applications, scalable backend services, APIs, and full-stack solutions. Integrate large language models, AI services, data platforms, and external APIs into customer-focused products. Collaborate with engineering, product, and cross-functional teams while contributing to cloud-native applications, data pipelines, distributed systems, testing, deployment, and production operations.

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

Develop and formalize methods to validate reported vulnerabilities and generate/verify safe patches for internal NVIDIA targets. Define correctness, regression, and revalidation standards; produce repeatable, auditable processes and evidence. Apply AI-assisted workflows cautiously, perform bounded applied research on vulnerability classes, and document findings for independent security review.

Build and evaluate LLM-powered agentic systems for security operations: implement features, create evaluation frameworks, automated benchmarks and regression tests, investigate failures, experiment with prompting/retrieval/planning, build infrastructure for deployment and monitoring, and collaborate with customers and product teams to improve agent performance and reliability.

Lead architecture and delivery of LLM-powered data platforms and pipelines: data extraction, transformation, storage, model integration, deployment and evaluation. Drive scalability, reliability, cost-efficiency, engineering standards, and cross-functional collaboration. Mentor engineers, lead architecture reviews, prototype and productionize ML capabilities, and ensure operational maturity (SLOs, observability, on-call). Translate business goals into data products for enterprise customers.

As a Senior AI Engineer, you'll design and deploy AI systems, own AI pipelines, and integrate LLMs into workflows, significantly impacting product direction.

Lead development of AI-native production systems: convert AI-assisted prototypes into scalable, secure software; own architecture, reliability, and technical direction; direct and control AI coding agents; define engineering standards and workflows; collaborate with founders on product and strategy; debug complex issues and elevate engineering quality.

Build and operate production-grade agentic AI systems and LLM-powered customer features. Design RAG pipelines over sensitive financial data, contribute to shared AI infrastructure (LLM services, agent orchestration, evaluation/monitoring), and own reliability, security, and compliance in production. Collaborate with product and design to deliver customer-focused AI solutions.

As a Senior Consultant, you will design and develop AI solutions using Anthropic models, support client engagements, and lead automation efforts while collaborating with delivery teams.

Design, develop, and deploy ML and deep-learning solutions (including generative AI). Lead end-to-end model lifecycle, build RAG/agentic workflows, integrate models into production, collaborate with engineering and clients, monitor performance, and document experiments and data provenance.

Design, build, and operate scalable cloud backend services that power LLM-based features (inference, RAG, agents, evals). Own end-to-end delivery including Terraform, CI/CD, monitoring, and incident response. Collaborate with product, UX, and platform teams and apply AI-assisted development practices daily.

Lead the architecture and development of AI orchestration platforms, multi-agent workflows, RAG systems, scalable data pipelines, and enterprise AI components. Build MLOps and LLMOps capabilities, responsible AI guardrails, governance, observability, evaluation, and telemetry solutions. Drive architecture reviews and cross-team alignment while enabling secure, auditable AI applications across software engineering functions.

Deploy, integrate, operate, and optimize high-performance NFS storage for GPU-accelerated Kubernetes and AI platforms. Configure Linux storage and networking, integrate CSI storage into k0s and K0rdent clusters, automate provisioning through Terraform/OpenTofu and GitOps, and build observability for capacity, performance, and reliability. Diagnose end-to-end storage issues across hybrid, edge, and air-gapped environments while establishing operational standards.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

Own end-to-end features across Go backend services and React/TypeScript frontend applications. Design, develop, and debug high-load distributed systems, APIs, integrations, and event-driven services. Use AI coding agents as part of daily development, critically reviewing and verifying generated output. Contribute to architecture decisions, resolve complex production issues, and improve engineering practices for AI-assisted delivery.

Build and operate Sysdig’s AI platform, including MCP servers, agent infrastructure, Bedrock runtime and gateway services, usage and cost pipelines, governance controls, and secure AI automations. The role requires production ownership, AWS expertise, full-stack development, containerized deployments, CI/CD, LLM application development, observability, access control, and strong independent execution.

Leads the design, development, and production integration of Generative AI solutions into distributed software systems. Evaluates AI models and tools, builds robust AI-enabled applications, leads design and code reviews, manages multiple sprint teams, mentors engineers, and partners with product, data science, and engineering teams to deliver scalable, measurable AI capabilities.

Drive technical sales for Azure cloud and AI infrastructure solutions. Lead architecture sessions, workshops, demos, proofs of concept, and migration or modernization initiatives. Design secure, scalable hybrid-cloud architectures for Windows, SQL, Linux, PostgreSQL, Oracle, and enterprise applications. Advise customers on networking, resiliency, compliance, containers, AKS, Azure Arc, SAP, VMware, and partner technologies while collaborating with engineering and business stakeholders.

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.