Top Remote AI Engineer Jobs

This role involves designing, deploying, and maintaining machine learning and AI models in production, managing ML pipelines, and ensuring system reliability and performance.

The AI Developer is responsible for designing and deploying AI solutions, including autonomous agents and predictive models, and integrating them into business operations.

The AI Engineer will develop scalable AI systems, optimize LLMs, maintain data workflows, and ensure system resilience while collaborating on innovative solutions.

The AI Engineer will build AI-powered applications, prototype solutions, implement workflows using LLMs, and manage sub-projects while improving internal tools.

The Applied AI Engineer will develop AI-powered systems to streamline internal processes, collaborating with various departments to enhance efficiency and automation. This role involves problem ownership, system design, and deploying solutions that leverage AI technology while ensuring user adoption and long-term value.

As a Senior AI Engineer at BioRender, you'll develop AI-driven features, collaborate with cross-functional teams, and prototype innovative solutions to enhance scientific communication.

Build and own end-to-end AI features across the stack: prompt engineering, evals, vector search, backend/data models, and frontend interactions. Ensure accuracy, safety, speed, and on-brand AI outputs. Collaborate with Product and Design to iterate on generative AI features used daily (coaching, candidate summaries, natural-language assistant).

Design and implement AI-driven tools to improve workflows across teams. Collaborate to identify internal problems AI can solve, and prototype solutions quickly.

Design, build, and integrate AI-enabled applications using C#/.NET and Azure. Develop data ingestion, transformation, and automation pipelines for structured and unstructured data. Implement and optimize LLM/Generative AI solutions, build APIs, and support end-to-end delivery from design through production handoff while partnering with business stakeholders.

Build and deploy AI-powered tools and workflows across performance marketing, creative production, CRM, landing pages, growth operations, and product. Develop LLM integrations, personalization systems, automation pipelines, dashboards, Slack bots, and internal tools using marketing platforms and data infrastructure. Partner with Growth, Product, and Engineering teams, evaluate AI tooling, mentor nontechnical users, and translate technical solutions into measurable business impact.

Own the design and front-end implementation of Chess.com’s internal growth, AI knowledge, agent, and admin platforms. Create polished UI/UX, translate designs into production-ready interfaces, improve interaction and motion quality, and evolve the design system. Prototype rapidly with Cursor and Claude, design AI-native workflows, collaborate across a distributed team, review code, and ship end-to-end frontend experiences.

Own strategic enterprise deployments of production agentic AI systems, including multi-turn voice and digital agents, retrieval pipelines, integrations, guardrails, monitoring, evaluation, and ROI measurement. Support pre-sales demonstrations and proof-of-concept builds while translating customer feedback into product and engineering roadmap improvements. The role requires hands-on production coding, customer-facing technical leadership, and comfort operating in an emerging forward-deployed engineering function.

Develop and deploy LLM workflows and production machine learning models for nuclear-system design and analysis. Build surrogate models for real-time digital twins, manage data pipelines, maintain rigorous information controls, and lead AI initiatives from inception through delivery. The role also involves growing a software engineering team, presenting roadmaps and demos to leadership, gathering user feedback, and collaborating with nuclear engineers in a fast-paced startup environment.

Lead customer-facing engagements to design, deploy, govern, and drive adoption of Microsoft 365 Copilot, AI agents, and cloud solutions. Advise on identity, security, compliance, governance, and responsible AI; develop adoption and change-management strategies; analyze usage and business impact; and coordinate partners to scale solutions.

Develop and productionize AI systems, including LLM applications, RAG, classification, summarization, NLP, speech, and conversational AI. Build Python backend services and APIs, automated evaluation and monitoring pipelines, responsible-AI safeguards, and cloud-native deployments on AWS and Kubernetes. Collaborate with product, medical, and engineering teams to optimize AI systems for latency, scale, reliability, and real-world healthcare applications.

Build and scale an AI-powered audit workbench that transforms raw evidence into reviewable workpapers. Responsibilities include designing ingestion and extraction pipelines, deterministic recomputation and exception detection, workpaper generation, evaluation infrastructure, and frontend review tools. The role emphasizes production LLM reliability, golden-dataset evaluations, schema design, traceability, deterministic regulatory logic, structured human review, and end-to-end system ownership in a regulated domain.

Lead AI initiatives, establish strategy, and develop generative AI applications. Collaborate with teams to build and optimize financial products.

Design, prototype, and validate agentic AI systems and LLM-driven workflows for federal health IT. Perform data exploration, integrate agents into cloud analytics platforms, implement MLOps/LLMOps practices, build demonstrations, and guide production deployments while mentoring developers and ensuring governance and security.

Lead design and implementation of high-performance agent runtimes, backend services, and self-hosted model/agent infrastructure. Build low-latency execution systems, real-time data pipelines, telemetry loops, CI/CD for zero-downtime deployments, and ensure fleet reliability on AWS/EKS.

As a Junior Backend Developer, you'll translate UI/UX designs into code, integrate with APIs, and utilize AI tools for efficiency.

Design and implement AI-driven solutions, collaborate with stakeholders, and scale AI knowledge across the organization. Foster AI adoption and develop scalable frameworks.

Lead end-to-end development of AI-enabled, full‑stack products and production AI Agent systems. Own architecture, APIs, observability, and deployments; collaborate with product and stakeholders; balance rapid prototyping with scalable engineering; integrate LLMs and agent loops; mentor others and ensure reliability, performance, and customer-focused UX.

As an AI Product Engineer, you will develop and deploy AI-driven software solutions, collaborate with customers, and optimize applications for real-world impacts.

As a Backend Engineer at Brain Co., you will design, build, and operate backend services and data pipelines for AI products, focusing on robustness and scalability. You'll collaborate across teams and own the full service lifecycle while ensuring high availability and performance. You should have 3+ years of experience in production backend systems and a strong foundation in APIs and distributed systems.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.