Top Remote AI Engineer Jobs

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

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.

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

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

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.

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.

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.

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.

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, 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.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Lead end-to-end GPU-focused Kubernetes deployments from bare metal to production-ready vCluster environments. Configure and troubleshoot GPU nodes (GPU Operator, CUDA, RDMA/InfiniBand), CNI and storage (CSI, Ceph/Rook/Weka/Longhorn), validate managed K8s, produce playbooks, transfer knowledge to customers, and feed infrastructure learnings back to Product and Engineering while supporting pre-sales proofs of value.

Build and own core backend infrastructure for long-running agentic AI workflows, ensuring high reliability, observability, disaster recovery, secure execution sandboxes, HIPAA-compliant data handling, and production-grade IAM and secrets management.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.

Design and implement backend services for an AI-driven automation platform, focusing on LLM orchestration, API development, and system reliability.

Design and build a production multi-agent harness and MCP integrations that operate across ERPs, CRMs, and business tools. Own end-to-end production concerns (reliability, observability, permissions, safe workflow execution), scope the product, make architecture decisions, and ship features working closely with CTO, product, engineering, and design. Stay current with agent frameworks and use tools like Claude Code in development.

The Internal Systems Engineer builds AI-powered tools and maintains a shared agent platform while integrating AI into customer operations at impact.com.

Lead production agentic development and build R&D tooling that accelerates team workflows. Ship weekly in the main codebase, create and improve custom agents, MCP integrations, evals, and codegen pipelines, train engineers on new workflows, and raise team standards for prompts, tooling, and autonomous throughput.

As an AI Prompt Engineer, you'll develop prompt templates, conduct A/B tests, collaborate with trainers, and maintain prompt libraries for generative AI applications.