Top AI Engineer Jobs

The Principal AI/ML Engineer will lead the development of AI/ML platforms, drive technology architecture, ensure quality and integration, and provide strategic leadership while managing a team of engineers.

Design, develop, and deploy state-of-the-art deep learning models for multimodal and satellite imagery; own ML architecture, infrastructure, model registries, and inference services; mentor the team, translate product vision into technical roadmaps, and integrate research into production-ready, scalable geospatial AI systems.

First Sales Engineer responsible for technical pre- and post-sales with enterprise finance customers. Design and ship agentic AI solutions, tailor proofs-of-concept to AR workflows and ERP integrations, and drive implementations to production. Act as technical SME to accelerate sales and product adoption.

Lead architecture and hands-on development of cloud-native AI web applications. Design end-to-end solutions integrating LLMs, vector search/RAG, real-time data, and scalable backend services. Implement model serving, RAG pipelines, AI agents, and ensure quality via testing and CI/CD while leading cross-functional engineering teams and translating business requirements into production-ready systems.

Lead design, development, and deployment of production-scale ML and AI systems. Own end-to-end ML lifecycle, architect scalable pipelines and infrastructure, mentor engineers, integrate emerging AI (LLMs, RAG, agents), optimize models for edge inference, collaborate with cross-functional teams, and ensure monitoring, reliability, and ethical considerations for law enforcement and homeland security solutions.

Owner of ServiceNow and Dynatrace automation and integrations to build AI-driven ITSM workflows. Design, develop, govern, and operate scripted automation, AI agents, CMDB, REST APIs, dashboards and reporting to enable proactive, autonomous incident, change, problem, and service request management across fleet and shoreside environments.

Own and engineer the ServiceNow automation and AI platform: design and build scripted automation, AI agents (Now Assist/Autonomous Workforce), Dynatrace-ServiceNow integrations, CMDB governance, REST APIs, and executive reporting to drive proactive, autonomous ITSM across fleet and shoreside environments.

Owner of ServiceNow automation and Dynatrace integration to transform ITSM into proactive, AI-driven operations. Design and build scripted automation, Flow Designer workflows, AI agents, CMDB governance, REST integrations, and executive reporting to reduce L1 manual work, improve MTTR, and enable autonomous operational outcomes across fleet and shoreside environments.

Design and implement complex backend services for Binance Square. Collaborate with product, DevOps, and QA to deliver high-quality microservices, promote engineering best practices, and perform code reviews.

Work as a hands-on junior ML engineer building generative AI apps, traditional ML models, and MLOps solutions. Implement and maintain model training, inference pipelines, and LLM systems while being mentored by senior engineers and delivering client projects.

Lead execution of Product Engineering AI and automation initiatives across photonics products. Build and deploy reusable AI/automation tools, transform engineering data into AI-ready systems, and drive cross-functional adoption to improve development, qualification, manufacturing, and reliability processes.

Early-career full stack engineer role to build end-to-end product features, integrate AI coding tools into the workflow, mentor peers, improve cross-team efficiency, and contribute across the product lifecycle from design through deployment.

Build and maintain full-stack web applications using React/Vue frontends and Java/Spring Cloud microservices. Design and consume REST/AI model APIs, work with SQL and NoSQL databases, use AI-assisted development tools, own features end-to-end, collaborate across product and design, ensure high-quality UI/UX, write tests, and contribute to documentation and architecture.

Support development of internal LLM-based tools (coding assistants, RAG pipelines, prompt optimization), improve engineering efficiency, test and document AI tools, and research emerging AI technologies.

Early-career role building AI-powered developer tools and internal LLM applications. Tasks include coding assistants, lightweight RAG pipelines, prompt optimization, testing, documentation, and researching emerging AI technologies to improve engineering productivity.

Build and enhance LangSmith frontend features: implement user-facing UI with React and TypeScript, create reusable components, translate designs into high-quality code, optimize performance, and collaborate with backend engineers and designers to improve observability and evaluation workflows for LLM applications.

Develop, debug, and maintain AI applications and tools for large-scale data analysis. Collaborate with engineers and leadership to design solutions, document procedures, and support users.

Build AI products and workflows using LLMs and foundation model APIs. Design AI agents, RAG pipelines, embeddings and vector search, integrate data and third-party APIs, build backend services, prototype-to-production development, and evaluate model performance for accuracy, latency, cost, and reliability.

Design, implement, and maintain CI/CD and MLOps pipelines, provision and scale cloud-native AI infrastructure (Kubernetes, GPU clusters), enforce IaC practices, implement observability and security frameworks, lead incident response and cross-functional platform engineering to ensure high availability and compliance.

Design, fine-tune, and deploy multimodal vision-language and VLA models for autonomous eVTOL aircraft; benchmark, validate in simulation/hardware/flight, and productionize models for edge deployment under safety and performance constraints.

Lead AI system safety assurance for humanoid robots: perform STPA/FMEA hazard analyses, assess model-update risks, define AI safety requirements (inputs, confidence, OOD), develop V&V test cases, deploy validation cells, maintain traceability, and produce evidence packages for certification.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Design, fine-tune, and deploy LLMs and RAG pipelines in private, secure environments. Build multi-agent systems, private inference infrastructure, evaluation frameworks, and observability tooling. Collaborate with platform teams to ensure secure, scalable production AI for enterprise customers.

Develop, train, evaluate, and deploy spatial generative AI models for home renovation and design. Define inference framing, prepare image-based datasets with labeling partners, iterate with user feedback, and deploy performant models into production architecture.