Top Remote AI Engineer Jobs

Design and implement runtime protocols, formats, and systems to capture, serialize, stream, and replay interactive 3D content across engines. Own engine subsystems from low-level C++ runtime to high-level C# APIs. Optimize for low latency, bandwidth, determinism, and memory; integrate AI systems to inspect and control engine state.

Lead design and implementation of agentic AI/ML solutions and modern software (APIs, microservices, CI/CD, streaming) on cloud platforms. Bridge legacy IBMi AS400 systems with modern AI, train and fine-tune models, oversee lifecycle, mentor engineers, set technical direction, and improve automation and testing across claims adjudication systems.

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

Lead end-to-end customer engagements to discover, scope, deploy, and realize value from AI agents. Run executive workshops, design and configure agentic solutions, create deployment playbooks, partner on integrations, and drive measurable business impact while shaping product direction.

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.

Build and operate the AI platform and runtime infrastructure for LLM/agent-powered products. Deliver CI/CD pipelines, model serving (self-hosted and managed), eval frameworks, key management, observability, cost controls, and developer tooling. Partner with AI, software, security, and finance teams and produce runbooks, docs, and best practices to ensure reliable, secure, and cost-effective AI deployments.

Design, build, and deliver AI-driven Salesforce Agentforce agents and intelligent automations. Develop integrations, test automations, debug Apex/JavaScript/Python code, produce technical documentation, mentor junior engineers, participate in DevOps and QA, and support transformational projects and high-priority incidents.

Build, maintain, and optimize cloud data pipelines and dbt models; design Airflow DAGs; implement dimensional models and BI dashboards; integrate healthcare data with HIPAA-compliant practices; contribute to RAG pipelines and MLOps workflows; review contractor code, monitor data quality, and document designs following governance standards.

This role involves designing, developing, testing, and maintaining software solutions, collaborating with various teams while focusing on scalable and reliable engineering.

Lead design and build of AI-driven enterprise contact center and CX solutions in pre-sales: translate workflows into prototype architectures, architect retrieval/orchestration/validation systems, run technical discovery, partner with account teams, and deliver production-grade proofs of concept that handle real-world constraints and KPIs.

The role involves leading the architecture, development, and scalability of fullstack systems for AI agents while mentoring engineers and making technical decisions.

The role involves designing and building fullstack systems for AI agents, along with research and collaboration across teams to improve user experience.

The role involves enhancing a graph compiler for ML models, optimizing performance across hardware platforms, and collaborating on core technologies.

Lead the design and implementation of AI agents and automation across pro-code and no-code platforms. Define repeatable automation patterns, integrate AI platforms with business tools via APIs, build RAG pipelines and vector DB integrations, write Python scripts for complex integrations, and guide technical decisions while working closely with the VP of AI to review agent performance and scale solutions.

Design and implement AI-powered educational software, focusing on generative AI systems, mentor peers, and ensure scalable, secure architecture.

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

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.

Design and build secure, scalable systems as a Full-Stack Software Engineer, leveraging expertise in Next.js, TypeScript, PostgreSQL, and AI tools to enhance cybersecurity within global infrastructure.

The Senior Full-Stack Software Engineer will design, build, and maintain secure, scalable web applications using modern technologies, with a focus on AI-assisted development and application security.

Senior technical leader responsible for architecting and building cloud-native, microservices-based platforms with AI capabilities. Lead design and delivery of scalable search and AI solutions (LLMs, RAG), data pipelines, search optimization (Elastic/OpenSearch/Solr), CI/CD and DevOps adoption, observability, and mentor engineering teams while driving responsible AI practices and stakeholder collaboration.

Lead design and implementation of scalable AI-centric systems for offensive security. Develop agentic AI capabilities for vulnerability identification and automated pentesting, own feature delivery from ideation to production, mentor engineers, run design/code reviews, and collaborate cross-functionally to integrate AI into platform systems.

As a Senior Full-Stack Software Engineer, you will design, build, and ship AI-powered systems to optimize workflows in the single family rental market, focusing on backend services and full-stack development in a fast-paced environment.

The Innovations and AI Solutions Engineer develops and optimizes AI solutions, manages data flows, implements machine learning models, and collaborates with users on data analysis and reporting.

Design and build the distributed systems layer powering a spatial computing platform: real-time coordination, persistent world state, APIs, AI platform infrastructure (vector search, retrieval, agent orchestration), and scalable services to support many concurrent users and third-party developers.

Lead Unity architecture and build persistent, multi-user spatial systems for mobile and wearables. Design spatial persistence, synchronization, AI-assisted content pipelines, and define long-term platform direction as a founding technical contributor.