Top AI Engineer Jobs

Design and implement AI/ML platforms, establish secure connectivity, build CI/CD pipelines, integrate conversational AI, and ensure security compliance in insurance processes.

The AI Behavior Engineer will measure and shape AI model behaviors, collaborating with stakeholders to conduct evaluations and adapt methods for various contexts.

The AI Research Engineer designs and optimizes AI systems for enterprise automation, collaborating across teams to improve efficiency and decision-making.

Responsible for architecting AIOS solutions, leading proof-of-concepts, collaborating with engineering teams, defining roadmaps, and delivering project requirements.

As a Senior AI Kernel Engineer, you will design and optimize high-performance kernels for AI inference on GPUs and custom accelerators, improving performance and collaborating with various teams to influence architecture and implementation.

Design and implement high-performance, scalable inference systems for LLMs and VLMs across cloud, edge, and hybrid platforms. Develop and optimize custom kernels for hardware accelerators (GPU/NPU/DSP), integrate tensor/model parallelism, caching, and quantization, and collaborate with system and hardware teams to deploy production-grade AI inference.

The Staff Software Engineer will architect and scale backend systems for AI workflows, ensuring robust performance and reliability in enterprise applications.

Design and develop AI software for autonomous robotics, focusing on computer vision and VLA models, while collaborating across functions.

As Lead ML Engineer, you will architect and build a conversational AI interface, optimize models for deployment, and collaborate with a multidisciplinary team.

Design and build AI-driven agents and large-scale backend systems to improve efficiency in revenue organizations, ensuring reliability and scalability during high-volume processing.

Design and build scalable, event-driven backend systems for AI agents, ensuring reliable agent logic, orchestration, and fault tolerance across workflows.

Lead design, build, and operation of GenAI product features and a shared AI platform: RAG pipelines, LLM-driven personalization, conversational agents, agentic workflows, eval frameworks, and MLOps. Own end-to-end production lifecycle, observability, on-call, and integrations with marketing/BI systems. Collaborate with personalization teams and document architectural and evaluation outcomes.

Lead end-to-end technical strategy and implementation for iHerb's AI Platform and GenAI products. Design and build shared AI infrastructure (RAG pipelines, vector search, embeddings, evals, MLOps), set architecture and quality standards, deliver production AI systems, coordinate cross-team technical efforts, and mentor senior engineers.

Lead end-to-end enterprise AI solutions for an LLM-powered coaching platform: design, implement, and evaluate prompts, memory/knowledge patterns, and coaching flows; architect reusable components; define evaluation/guardrails; collaborate with Product and AI Engineering; mentor colleagues and measure deployment outcomes.

Lead design and delivery of high-priority AI initiatives across product and platform boundaries. Rapidly prototype and launch AI features, unblock teams, mentor engineers, and shape production-grade LLM systems (retrieval, evals, prompting, orchestration). Collaborate with product, design, and domain experts to turn customer needs into shipped product.

Design, implement, test, and maintain scalable C++ software for autonomous vehicle and perception systems. Work across full SDLC, support vehicle testing, simulation-based regression, data acquisition/analysis from remote vehicles, root-cause analysis, and mentor engineers while collaborating with ML researchers and cross-functional teams.

Drive customer success for AI observability by architecting and delivering proofs-of-value, building integrations (data pipelines and ML infra), advising on model monitoring and governance, and championing customer needs across product, engineering, and sales to drive adoption and expansion.

The Software Engineer will develop and enhance infrastructure and user-facing functionalities in cloud-native simulation platforms, collaborate on technical projects, and support AI and backend system initiatives.

Lead design, prototyping, and operationalization of AI/ML and LLM-enabled capabilities for intelligence workflows. Collaborate with stakeholders to deliver production-ready systems, shape technical strategy, and automate mission analysis using scalable software architectures.

Develop and implement automation for ML/LLM integrations and data pipelines to support incident response and automated malware analysis. Build ingestion, transformation, monitoring, API integrations, and scale automation from pilot to production while ensuring data quality and documenting solutions.

Design, build, and maintain end-to-end integrations between Commures platform and third-party healthcare systems (EHRs/EMRs). Implement scalable, reliable solutions using LLMs and modern cloud tooling, ensure HL7/FHIR and HIPAA compliance, troubleshoot production issues, mentor junior engineers, and drive integration platform architecture and monitoring.

The Data Engineer will develop and optimize data pipelines for LLMs, ensuring high-quality healthcare datasets while collaborating with data scientists on model requirements.

Lead design and ownership of an AI-native web platform: web framework, design system, CMS, CI/CD, infrastructure, rendering and edge delivery. Define architecture and agentic SDLC, build evaluation guardrails, mentor engineers, and drive platform-wide technical alignment and migrations.

Build and scale web platform infrastructure (CI/CD, deployment automation, secrets management, edge delivery) for databricks.com. Implement automated accessibility testing and centralized consent management, unify frameworks into a shared design system, and operate within an AI-native, agent-assisted SDLC to drive platform features to production.

Design, build, and operate scalable data and AI platform capabilities. Create reusable abstractions, templates, and services for data pipelines and products. Integrate Generative AI for discovery and automation, ensure data quality, lineage, observability, security, and support cross-functional analytics and applied-AI teams.