Top AI Engineer Jobs

Join Capsule as an AI Engineer to develop ML models for video editing. Responsibilities include researching ML techniques, collaborating on feature development, and optimizing model performance.

Lead the AI team, develop AI solutions for semiconductor processes, enhance tool operations, and mentor engineers. Define strategies and architectures for AI integration.

The Applied AI Engineer collaborates with clients to define AI strategies, identifies high-impact use cases, and builds production-ready AI systems, focusing on customer needs and delivering tangible business value.

Seeking a Senior AI Engineer to develop AI solutions, oversee project delivery, and collaborate on system architecture. Requires 5+ years with Python and AI technologies.

Design and build scalable AI infrastructure, automate workflows, optimize GPU workloads, and ensure reliability for AI systems at Eloquent AI.

Build and productionize GenAI features: design experiments, evaluation suites, advanced RAG and reasoning pipelines, oversee SFT/LoRA tuning, and optimize quality-cost-latency trade-offs for reliable, user-focused AI products.

As an Applied AI Engineer at Rillet, you will create and implement AI systems for financial data, oversee features from design to production, and collaborate closely with product teams to innovate solutions in ERP workflows.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Build and deploy production LLM/ML pipelines for patent workflows: retrieval, multi-step prompt pipelines, evals frameworks, fine-tuning, agentic workflows, monitoring, and cloud deployment. Collaborate with attorneys and cross-functional teams to convert domain expertise into robust, scalable inference and data solutions.

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

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

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

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

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.