Top AI Engineer Jobs

Architect and build ML systems for generative AI: design training pipelines, evaluation and serving infrastructure, train and deploy diffusion and LLM models, ensure production reliability, data pipelines, and set technical direction and quality standards across the AI team.

Design and implement advanced AI systems, lead development teams, mentor junior staff, and ensure ethical AI practices while aligning strategies with business goals.

Design and deliver agentic systems for enterprises, integrating tools and data while ensuring safety and compliance. Collaborate with clients to optimize business workflows and system architectures.

Own applied AI end-to-end: select and operate foundation models, build Dossbot for natural-language ops and automation, create AI-assisted schema/workflow tooling, design permission-aware agentic automations, and implement evals and observability for safe production use.

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

Design, develop, and deploy production-grade AI/ML solutions and MLOps pipelines. Architect cloud/edge and microservice-based infrastructures, implement monitoring/drift detection and automated retraining, collaborate with data scientists and business stakeholders to deliver scalable AI that drives business value.

Design and build an AI-driven account management and optimization platform using LLMs and agentic systems. Create diagnosis and recommendation models, ensure reliable long-running services with observability and safeguards, and collaborate with ad delivery and product teams to improve advertiser performance and revenue.

The Staff AI Engineer will build, deploy, and maintain AI applications, manage data pipelines, and lead technical designs while mentoring engineers.

Design and build native Android applications for robot data annotation, dataset review, and experiment tracking. Create fast, reliable mobile experiences for real-time data capture, integrate AI models to automate annotation, and collaborate with AI researchers, robot operators, and web tooling teams to ensure consistent workflows.

The Data Infrastructure Engineer will design, build, and maintain data tools for robot data management, supporting AI researchers and ensuring optimal data solutions across storage and compute resources.

Lead enterprise AI programs, mentor engineers, define quality metrics, and deliver scalable AI solutions while collaborating across teams and advising stakeholders.

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.

Build and own an internal AI-powered recruiting pipeline: end-to-end LLM-based sourcing, screening, AI interviews, evaluation logic, and deep integrations with ATS and scheduling to automate hiring workflows.

Build and own the agent runtime, model orchestration, and evaluation infrastructure. Design ensemble routing, productionize frontier agent capabilities, implement tool and memory loops, and partner with applied teams to convert practitioner feedback into model, prompt, and routing improvements. Set hiring bar and engineering culture.

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

Design, build, and operate scalable, secure multi-cloud infrastructure and AI-focused platforms. Lead IaC automation (Terraform/Ansible/CloudFormation), enable MLOps workflows for model training and inference, apply AI-assisted engineering to reduce toil, drive platform strategy, deliver large-scale projects, and mentor engineers.

Design and implement scalable platform services, runtimes, and developer tooling for deploying and managing AI workloads on Arm-based edge devices. Build abstractions, observability, and performance optimizations across heterogeneous hardware while collaborating with architects and AI engineers to produce robust, maintainable systems and SDKs for on-device AI.

Build and scale backend and full-stack systems powering patient booking, clinician routing, logistics, and device integrations. Own 0->1 projects, design APIs and data models, solve scheduling and dispatch challenges, integrate external health systems, and collaborate with product, data, ops, and clinical teams.

Build and scale backend and full‑stack systems powering patient booking, clinician routing, logistics, and device integrations. Ship end-to-end features, design APIs and data models, solve scheduling/dispatch challenges, integrate with external health systems and medical devices, and own projects from 0→1 while collaborating with product, data, ops, and clinical teams.

As an AI Forward Deployed Engineer, you'll manage customer onboarding by integrating AI Agents, architecting solutions, and ensuring project success through strategic problem-solving and communication.

Build Town’s greenfield AI context and retrieval infrastructure, including hybrid lexical and semantic search, durable data models, realtime and batch pipelines, indexing, and storage. Optimize latency, cost, freshness, completeness, and reliability at scale. Establish the foundation for a knowledge graph connecting people, companies, projects, and content, while taking systems from prototype through production.

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.