Top AI Engineer Jobs

Build and deploy production machine learning applications, APIs, inference services, and data platforms. Engineer large-scale pipelines using distributed technologies, data modeling, ETL/ELT, transformation, and data quality practices. Translate defense and mission needs into scalable solutions across cloud, containerized, and GPU/CPU environments while collaborating with engineering, data, and mission teams.

Leads architecture, development, and delivery of AI-powered applications and enterprise data platforms. Designs Generative AI solutions using LLMs, agents, RAG, and Vertex AI; develops full-stack cloud-native applications; builds Databricks, Spark, and Lakehouse data architectures; and establishes GCP cloud standards. Provides technical leadership, mentoring, architecture guidance, governance, observability, CI/CD practices, technical evaluations, and technology roadmaps across engineering, data, analytics, and product teams.

Designs, develops, deploys, tests, monitors, troubleshoots, and maintains cloud and on-premise integrations using MuleSoft, Workato, or similar iPaaS platforms. Translates client requirements into integration and transformation solutions, supports production environments, documents implementations, leads SIT and UAT activities, manages change support, resolves integration issues, and mentors developers and architects.

Designs, deploys, and supports production AI/ML solutions across data ingestion, feature engineering, model training, MLOps, monitoring, and lifecycle management. Builds AWS cloud-native applications, generative AI systems, knowledge graphs, and real-time or batch pipelines. Partners with stakeholders on analytical solutions, ensures data quality and AI governance, leads technical design, and mentors engineers.

Partner with enterprise customers to identify AI use cases, architect and implement custom applications and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI concepts into actionable solutions, collaborate with Product and Engineering, improve technical integration processes, and drive customer adoption, time-to-value, and measurable business outcomes.

Partner with enterprise customers to identify AI use cases, architect and implement custom applications, templates, and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI concepts into solution recommendations, collaborate with Product and Engineering, develop scalable integration processes, and drive customer adoption, time-to-value, and business outcomes.

Partner with enterprise customers to identify AI use cases, validate feasibility, and implement tailored solutions using WRITER’s platform, APIs, and Knowledge Graph. Architect applications, templates, and integrations; explain AI capabilities to technical and executive audiences; collaborate with Product and Engineering; develop scalable integration processes and documentation; and drive customer adoption, expansion, and measurable business outcomes.

Build secure, scalable generative AI services, applications, APIs, microservices, and agentic workflows. Own architecture from proposal through production, develop React and TypeScript interfaces, and collaborate with product, data science, and DevOps teams. Implement deployment, monitoring, logging, enterprise security, and reliability capabilities while mentoring engineers and delivering resilient production systems.

Develop and deploy large-scale computer vision and machine learning models for Coupang’s search and product experiences. Responsibilities include experimentation, statistical analysis, scalable model development and serving, A/B testing, researching advanced vision techniques, presenting results to leadership, and mentoring engineers. The role requires applying deep learning, transformers, generative modeling, and other state-of-the-art approaches to user-facing products.

Design and operationalize AI/ML capabilities for secure DoD environments, including agentic workflows, production APIs, MLOps, model deployment, monitoring, validation, and continuous improvement. Integrate structured and unstructured data with Foundry applications and mission systems. Collaborate with engineering and mission teams, troubleshoot production systems, and apply responsible AI, security, governance, and model-management practices.

Architect the infrastructure powering Pinterest’s Generative AI and recommender systems at petabyte scale. Set the AI Platform’s technical vision and roadmap, lead cross-functional initiatives spanning data orchestration, model training, fine-tuning, evaluation, feature stores, and high-performance inference, and drive company-wide architecture decisions. Foster an inclusive engineering culture while maintaining high standards for quality, ownership, and accountability.

Leads AI security, platform modernization, and developer productivity initiatives. Designs enterprise solutions, manages AI vulnerability remediation, develops CI/CD and DevSecOps automation, modernizes Java platforms, enables rapid security releases, and establishes secure AI development standards. Partners with architecture, security, infrastructure, and engineering teams to deliver secure, scalable platforms and improve software delivery efficiency.

PhD summer intern developing AI agents with LLMs, deploying deep learning inference models through Python and FastAPI, integrating AI into R&D workflows, processing datasets, and delivering AI-driven insights. The role requires collaboration with R&D teams and is a full-time, 12-week, in-office internship in Mason, Ohio.

Architect and build production agentic AI systems for customer support and compliance automation. Lead technical direction for distributed systems, APIs, orchestration frameworks, guardrails, and evaluation systems. Create reusable automation primitives, improve grounding and reliability, reduce hallucinations, and mentor engineers on LLM application design and production readiness. Collaborate cross-functionally to scale AI automation across Coinbase.

Build backend systems that translate computer vision and machine learning outputs into interactive product experiences. Responsibilities include designing perception-to-product data contracts, shipping AI-generated geometry to a canvas, balancing accuracy with user experience, closing human correction loops, and bridging AI/ML and product engineering teams. The role requires Python and TypeScript, familiarity with PyTorch and model postprocessing, data contract versioning, and experience deploying computer vision or ML outputs in user-facing products.

Drives early supplier involvement, hardware co-design, DFM/DFT, manufacturability, and supply-chain readiness for AI data-center infrastructure. Responsibilities include liquid cooling, high-speed PCBAs, power delivery, rack integration, prototyping, NPI validation, supplier reviews, cost modeling, yield improvement, and second-source qualification. The role bridges system architects, engineering teams, and strategic suppliers to reduce technical and production risks while accelerating time to market.

Designs and develops APIs, system integrations, and AI-agent workflows for enterprise applications. Builds integrations with platforms such as Salesforce and REST APIs, deploys and supports solutions on AWS, and develops AI-powered automation using LLMs, tool calling, and multi-step workflows. The role includes client requirements gathering, troubleshooting, reliability optimization, cross-functional collaboration, and mentoring junior developers.

Designs, develops, tests, deploys, trains, monitors, and improves machine learning models for business and national security mission requirements. Responsibilities include selecting datasets, statistical analysis, algorithm execution, model evaluation, data pipeline integration, system testing, and scalable solution development. The role requires strong understanding of AI/ML architectures, application development, infrastructure, data engineering, and governance, with potential leadership of distributed teams.

Build and own production AI agents end to end, from tool and prompt design through evaluation, frontend experiences, deployment, monitoring, and continuous improvement. Develop full-stack services and shared AI infrastructure, debug retrieval and model issues, measure production performance, contribute to architecture and documentation, review code, mentor engineers, and collaborate with product, design, founders, and customers.

Build and own production AI agents end to end, including tools, prompts, retrieval, evaluations, backend services, and Word or web interfaces. Ship cloud-native features, maintain evaluation and observability systems, debug model and retrieval issues, and contribute to reliable infrastructure. Collaborate with founders, product, design, customers, and engineers while mentoring teammates and driving continuous production improvements.

Designs, develops, evaluates, and deploys machine learning models for sensor-data event detection, classification, and characterization in classified national-security environments. Builds end-to-end ML pipelines, integrates capabilities into operational software, evaluates failure modes, collaborates with scientists and analysts, and mentors junior developers. The role requires production-quality Python, deep knowledge of modern ML architectures, technical documentation, and active Top Secret clearance with eligibility for SCI and CI Polygraph.

Own FloQast’s multi-region AI platform infrastructure across AWS, including Bedrock model runtimes, sandboxed code execution, Terraform, observability, cost controls, CI/CD, security, and reliability. Support Transform, AI Matching, and AutoBuilder by managing scaling, throttling, tenant isolation, journey-based SLOs, model and prompt delivery gates, and AI-specific on-call processes across US, EU, and AU regions.

Build Placer.ai’s internal AI platform, including autonomous agents, MCP servers, SaaS and internal-tool integrations, authentication infrastructure, Claude plugins, Databricks data pipelines, usage analytics, and scalable platform infrastructure. Establish connector standards, optimize costs, triage integration requests, and implement security and compliance controls while partnering across engineering, data, operations, and business teams.

Leads development and architectural evolution of an AI-powered code security scanner. Responsibilities include building user-facing dashboards and workflows, implementing secure authentication, extending backend scanning and orchestration services, integrating LLM-based security analysis, automating vulnerability validation and reporting, enhancing scalability, and mentoring junior engineers.

Develop scalable AI-enabled software integrating machine learning models, APIs, data pipelines, retrieval systems, and agentic workflows. Build backend services and cloud-native applications using Python, AWS, Docker, and Kubernetes. Operationalize machine learning capabilities, improve system performance and reliability, and support model inference and information retrieval in mission environments. Collaborate with engineers and researchers while following software development, testing, and quality practices.