Top Remote AI Engineer Jobs

Design, build, and evaluate AI systems, focusing on natural language understanding and automation. Collaborate with teams to ensure production quality and performance metrics.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

Lead end-to-end development of AI-native financial products for credit union members. Use and improve AI agents, build features across a TypeScript stack, integrate with third-party APIs, monitor production, and partner with design and stakeholders to deliver reliable, scalable solutions.

Design, build, and run data-heavy features for financial products, improve data models, pipelines, migrations, and multi-tenant systems. Work in a TypeScript monorepo, operate serverless AWS infrastructure, integrate LLM APIs, monitor production, and lead pragmatic technical decisions.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

Own evaluation, selection, and continuous optimization of LLMs powering the analytics platform. Build Eval frameworks, benchmark and migrate models, implement RAG and embedding-based pipelines, optimize cost/performance, deploy model changes to cloud production, monitor performance and drift, and document methodologies and runbooks. Collaborate with product and operations and take end-to-end ownership of AI engineering work.

Own evaluation, selection, and continuous optimization of LLMs and AI pipelines for legal and medical document tasks. Build Eval frameworks, implement RAG and embedding-based systems, integrate and transition models into production on cloud, monitor performance and cost, and document runbooks. Collaborate cross-functionally and take end-to-end ownership of deployments and validation.

Own evaluation, selection, and production optimization of LLMs and AI pipelines for legal and medical document tasks. Design Eval frameworks, benchmark models for accuracy, cost, and speed, implement model transitions, build and deploy RAG and embedding-based solutions on cloud, monitor production performance, and maintain documentation and runbooks with end-to-end ownership.

Own evaluation, selection, and continuous optimization of LLMs and AI processes powering the analytics platform. Build EvalOps frameworks, benchmark and transition models, implement RAG/embedding/vector DB solutions, optimize orchestration pipelines, deploy model changes end-to-end, monitor performance/cost/drift, and document runbooks. Collaborate cross-functionally and execute production deployments.

Own evaluation, selection, and continuous optimization of LLMs and AI pipelines. Build EvalOps frameworks, benchmark models, implement model transitions, optimize RAG/document understanding pipelines, deploy and validate production changes, monitor performance/cost, and document runbooks.

Design, build, and operate distributed GPU training infrastructure for large neural networks. Optimize multi-node/multi-GPU execution, diagnose compute/memory/network bottlenecks, improve stability and fault tolerance, and partner with research teams to productionize large-model training pipelines for reliable, high-throughput training at scale.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

Develop and deploy machine learning solutions for construction-site understanding using computer vision, multimodal AI, VLMs, LLMs, generative AI, RAG, and agent architectures. Curate high-quality datasets, optimize models for performance across hardware targets, and build production ML pipelines in the cloud. Own components from design through deployment and monitoring while collaborating in an agile team.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

As a Fullstack AI Engineer, you'll prototype frameworks and scale real-time engines, focusing on product feel without handed specs.

Lead API product direction for search and retrieval primitives, partner with strategic accounts to architect integrations, translate customer insights into product specs and roadmaps, run technical deep-dives and documentation, and prototype and ship production code to improve developer experience and platform reliability.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

Design, implement, validate, and deploy advanced machine learning and statistical models across the data science pipeline. Collaborate with product and engineering, mentor peers, translate research into production, and deliver high-impact business insights using large-scale and distributed data platforms.

Partner with enterprise AI teams to design, implement, and scale production-grade generative AI observability and evaluation programs. Lead technical discussions, build custom integrations and tooling, manage multiple client engagements from scoping to delivery, and drive best practices across cross-functional teams to influence product and expand customer impact.

Build responsive React + TypeScript frontends and scalable Python/Rust backend services and APIs. Integrate with cloud (AWS/GCP/Azure), design databases, CI/CD, tests, and monitor reliability, performance, security, and cost. Optionally develop LLM/RAG/embeddings/vector database AI features.

Build end-to-end AI-native features: responsive React/TypeScript frontends, Python/Rust backends, cloud integrations (AWS/GCP), databases, CI/CD, observability, and optionally LLM/RAG/embeddings-based AI features while improving reliability, performance, security, and cost.

Build and launch AI-powered prospecting agents, SDR tools, lifecycle automations, onboarding flows, and internal products. Integrate CRM, sales, and marketing platforms using workflow tools, APIs, scripts, and LLMs. Own lead scoring, routing, SDR-to-AE handoffs, and lifecycle optimization. Measure impact on pipeline, meetings, activation, retention, and CAC payback while partnering with Sales, Lifecycle, Data, and Engineering teams.

Build and productionize end-to-end AI-powered product features: prototype and iterate on prompts, retrieval flows, evals, backend systems, APIs, data models, and UX to deliver reliable, customer-facing LLM workflows.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

Own product areas for KIRA’s AI Stockbroking and other financial services. Define user problems, product scope, success metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products. Use research, feedback, metrics, experiments, and production insights to launch features, monitor performance, and drive continuous improvement while balancing speed, experience, compliance, and operational complexity.