Top AI Engineer Jobs

The role involves designing AI/ML solutions for national security, capturing user workflows, and integrating systems in classified environments, requiring collaboration with various stakeholders.

Build and operate production private AI platform capabilities (agentic/multi-agent systems, orchestration, auto fine-tuning, runtime/model optimization). Implement evaluation, monitoring, safety/rollback controls, developer-facing APIs, and tooling. Partner with data and infra teams to ensure reliable, performant AI inference in constrained edge environments.

Lead the CRM AI team to drive technical direction and execute the roadmap for an AI-powered CRM platform, focusing on architecture, operational excellence, and cross-functional collaboration.

The Senior AI/ML Engineer will develop and improve analytical tools, approaches, and architecture for scalable AI and machine learning solutions, collaborating with engineers and data scientists on innovative projects.

The Senior AI/ML Software Developer will design, develop, and maintain AI applications, collaborating with teams and solving complex problems.

Design, build, and maintain LLM integrations and AI features; own end-to-end delivery from requirements to production monitoring; ensure scalability, reliability, observability; leverage AI agents to improve developer and operational efficiency; collaborate cross-functionally.

The Lead Infrastructure and Reliability Engineer will enhance GPU operations, define scalability strategies, and develop organizational strengths in a high-demand AI infrastructure setting.

Lead technical vision and roadmap for Pinterest's AI Platform. Architect petabyte-scale data orchestration, model training/fine-tuning, and high-performance inference for Generative AI and Recommender Systems. Drive cross-functional initiatives, influence architecture across the company, and cultivate an inclusive engineering culture focused on quality and ownership.

Own product areas end-to-end: discover, prototype, ship, and iterate. Build secure, well-tested, observable full‑stack systems; partner with product, design, data, and clinical teams; use data and experimentation to validate impact; improve AI‑native development practices and engineering systems while balancing speed, safety, and reliability in a regulated healthcare context.

As a Senior Machine Learning Engineer, you will architect a scalable ML platform, collaborate across teams, and ensure best practices for ML workflows and operations.

Profile, benchmark, and optimize large-scale LLM training and inference across GPUs, CPUs, HCAs, and switches. Focus on collective communication and high-performance networking, build PyTorch trace-based profiling and replay tools, define performance test plans, and collaborate across hardware and software teams to identify bottlenecks and achieve performance targets.

Design and build AI-native, high-performance CI/build/test/validation infrastructure for large Tegra C/C++ codebases. Optimize build graphs, caching, incremental builds, remote execution, device-lab testing, and integrate reasoning agents for autonomous triage, remediation, and self-healing workflows. Package reusable platform capabilities, drive adoption, and measure developer feedback loop improvements.

The AI Data Engineer will design and implement data pipelines, build AI-optimized data infrastructure, ensure data quality, and collaborate with teams to support AI applications.

Build and own AI product and platform capabilities across Spiral and AXO. Design and implement LLM-powered workflows, high-performance APIs, async workers, and data pipelines. Ship customer-facing features, improve reliability/observability, operate systems on AWS/GCP, and collaborate across engineering, product, and design to turn ambiguous ideas into production AI systems.

Embed with enterprise clients to design, build and operationalize agentic AI solutions in production: scope use cases, design architectures, write production-grade code, build retrieval pipelines, agent orchestration, evaluation suites, observability, and integrate with enterprise systems while handing over to support and abstracting reusable patterns.

Embed with enterprise clients to design, build, and operationalize agentic AI solutions in production. Lead discovery, architecture, production-grade coding, retrieval pipelines, agent orchestration, governance, evaluation suites, observability, and secure integrations. Drive cutover, handover to support, escalation, and produce reusable patterns and accelerators.

Design, build, and operationalize production-grade AI/Machine Learning solutions and MLOps services. Deploy models and API services to AWS and GCP using CI/CD and IaC, implement workflow automation, monitor post-production performance, research generative/ML techniques, and mentor junior engineers while collaborating with cross-functional data, cloud, and architecture teams.

Owner of quality for an LLM-based multi-agent claims adjudication pipeline. Build evaluation frameworks, automated LLM-grading workflows, function-call and reasoning-trace auditing using Vertex AI. Integrate CI/CD regression, Auto-SxS comparisons, and large-scale mocking to ensure compliance and accurate adjudications for DOL/DOI audits.

Lead design and implementation of agentic AI systems for claims adjudication using Gemini and Vertex AI. Build production-grade Python agents, implement RAG, grounding, stateful orchestration with human review, input/output guardrails, and Google Cloud DLP integration. Drive backend patterns, optimize model behaviors, and supervise fine-tuning to ensure reliable, auditable outputs in healthcare claims workflows.

Develop and maintain backend services for an MDM platform using Java, Spring Boot, and REST APIs. Build data ingestion, transformation, and integration pipelines, apply observability and testing best practices, optimize performance, and use AI-assisted tools to accelerate development. Collaborate in Agile teams across frontend, QA, product, and platform.

Support DISA by researching and applying AI technologies (ML, NLP, CV), developing and evaluating models, and aiding proofs-of-concept. Lead technical planning, quality/configuration management, produce documentation and briefings, and coordinate stakeholders to support RMF/STIG cybersecurity initiatives and mission outcomes.

Design and build AI-enabled solutions to accelerate analog circuit design, sizing, and layout. Develop and train discriminative and generative models (including LLMs and transformers), build data pipelines, test and monitor models, collaborate with analog design partners, participate in design reviews, and produce technical documentation for production-ready deployments.

Design, build, and tune hybrid retrieval and RAG systems combining Elasticsearch, vector/embedding search, and re-ranking models. Improve relevance using query analytics, behavioral signals, experimentation, and evaluation metrics to enhance search quality and Generative AI responses. Collaborate with AI engineers, platform teams, and product stakeholders.

Architect and ship production AI agent systems and modeling infrastructure for regulated finance. Build Python/FastAPI tooling, distributed event-driven backends, validation/evaluation pipelines, and compliance/auditability features. Influence system architecture, mentor engineers, and ensure reliable, explainable AI outputs in production.

Design, build, and maintain secure on‑prem and cloud infrastructure and CI/CD pipelines for Kubernetes-based services. Automate deployments, manage production/staging/dev environments, monitor logs and system health, enforce security controls, and support accreditation (A&A) and operational readiness. Troubleshoot incidents, perform root cause analysis, maintain documentation, and collaborate with Agile teams to drive DevSecOps best practices.