Top Remote AI Engineer Jobs

Lead design and implementation of cloud-first, AI-integrated full-stack applications on GCP. Drive rapid prototyping and MVPs, mentor developers, define architecture, maintain CI/CD pipelines, and collaborate cross-functionally to deliver production-ready solutions.

As a Senior AI Kernel Engineer, you will design and optimize high-performance kernels for AI inference on GPUs and custom accelerators, improving performance and collaborating with various teams to influence architecture and implementation.

Lead design, build, and operation of GenAI product features and a shared AI platform: RAG pipelines, LLM-driven personalization, conversational agents, agentic workflows, eval frameworks, and MLOps. Own end-to-end production lifecycle, observability, on-call, and integrations with marketing/BI systems. Collaborate with personalization teams and document architectural and evaluation outcomes.

Lead end-to-end technical strategy and implementation for iHerb's AI Platform and GenAI products. Design and build shared AI infrastructure (RAG pipelines, vector search, embeddings, evals, MLOps), set architecture and quality standards, deliver production AI systems, coordinate cross-team technical efforts, and mentor senior engineers.

Design, implement, test, and maintain scalable C++ software for autonomous vehicle and perception systems. Work across full SDLC, support vehicle testing, simulation-based regression, data acquisition/analysis from remote vehicles, root-cause analysis, and mentor engineers while collaborating with ML researchers and cross-functional teams.

The Data Engineer will develop and optimize data pipelines for LLMs, ensuring high-quality healthcare datasets while collaborating with data scientists on model requirements.

Design, build, and operate scalable data and AI platform capabilities. Create reusable abstractions, templates, and services for data pipelines and products. Integrate Generative AI for discovery and automation, ensure data quality, lineage, observability, security, and support cross-functional analytics and applied-AI teams.

Partner with Sales to design and demo AI-powered, data-driven solutions for life sciences clients using Komodo's Healthcare Map. Run live no-code analyses, build reusable demo assets, navigate AI governance and procurement, quantify ROI, mentor junior SEs, and feed product/AI roadmap input.

Build and maintain secure RESTful and event-driven APIs and MCP server implementations to allow AI agents to interact with enterprise platforms (ServiceNow, Okta, ERP). Implement authentication (OAuth2/OIDC), API gateway policies, audit logging, and monitoring. Collaborate with AI and governance teams, document integrations, and ensure secure, reliable, auditable agentic workflows.

The Data/AI Cloud Engineer will implement AWS data services, build ETL pipelines, optimize databases, and support data-driven solutions collaborating with technical teams.

The AI Automation Engineer will apply AI in finance operations to automate processes like AP, AR, and reconciliation, ensuring compliance and performance measurement.

You will build and maintain AI-native application features, working with a team of AI and backend engineers to ensure quality and innovation.

The role involves architecting scalable backend systems for real-time evaluations, integrating with streaming data, and building intervention layers for AI applications. You'll design frameworks and operationalize vector databases while ensuring low-latency service performance.

Build end-to-end features across frontend and backend for the Mem0 platform using Next.js and Python. Design APIs, UIs, backend services, and data models; optimize performance and reliability; write tests and operate production systems; collaborate with design, research, and customers to deliver scalable AI memory product experiences.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

Build and productionize backend-first AI capabilities for a FinOps intelligence platform: design backend services, implement AI features (anomaly detection, recommendations, agents), create evaluation/rollback/monitoring systems, and collaborate with product to ship reliable, cost-efficient, production-ready features.

Embed with client engineering teams to operationalize LLMs and retrieval systems across multi-cloud environments. Build agentic solutions using Vertex AI Agent Builder and ADK, connect agents securely to BigQuery/Spanner via MCP, implement streaming pipelines (Dataflow) for real-time inference, and manage model training, tuning, deployment, and evaluation on Vertex AI. Provide consultative guidance, cross-functional collaboration, and clear stakeholder communication to ensure business value from Generative AI solutions.

Lead end-to-end delivery of AI-powered applications: define requirements with clients, translate business needs into technical plans, coordinate cross-functional engineering teams, leverage AI tools for prototyping and delivery, track progress, remove blockers, and drive rapid iteration and continuous improvement.

Lead architecture and technical strategy for Lyra's ML and generative AI platform. Design training, fine-tuning, and low-latency inference infrastructure (RAG, vector DBs), set ML/AI SDLC standards (dataset lineage, CI/CD, evaluation, monitoring), partner with cross-functional clinical and product teams, mentor senior engineers, deliver prototypes and critical code, and ensure secure, reliable production ML systems.

Design, implement, and evaluate novel AI systems (LLMs, automated reasoning, knowledge acquisition) for assessing journalism. Translate research ideas into scalable prototypes, run experiments, adapt/fine-tune models, develop new algorithms and evaluation methods, and communicate results via reports and papers.

The role involves rapid prototyping and architecting production LLM pipelines for AI in marketing workflows. Responsibilities include developing proofs of concept, making AI architecture decisions, building production data agents, and understanding user workflows for seamless AI experiences.

The Senior Software Engineer will design and build fullstack features using Go, React, and Ember, ensuring optimal customer experiences and system scalability while mentoring peers and driving projects.

As an Applied AI Engineer, you'll build AI agents and experiences from concept to production, collaborating across teams and optimizing systems for usability and quality.

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.