Top AI Engineer Jobs

Develop cloud software and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java software development, RESTful services, distributed systems, cloud integration, infrastructure as code, containerization, testing, sustainment, and issue resolution. The role requires experience with Hadoop and big-data technologies, Linux, software integration, technical documentation, and secure solutions supporting national security missions.

Develops and leads systems engineering activities for cyber intelligence and AI/ML capabilities hosted on large-scale compute clusters and AWS Cloud. Responsibilities include requirements analysis, system architecture and design, interface definition, technical documentation, risk and configuration management, systems integration and testing, verification planning, and security and accreditation support. The role provides technical leadership across integrated product teams and coordinates with government stakeholders to ensure system technical integrity.

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, deployment, enhancement, monitoring, and issue resolution. The role uses distributed NoSQL and big-data technologies, cloud architecture, AI/ML tools, microservices, containers, and infrastructure-as-code. Candidates must have at least two years of relevant software engineering experience and an active TS/SCI clearance with polygraph.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models supporting business and national security mission requirements. Integrates data pipelines, evaluates model performance, ensures data integrity, and transforms prototypes into scalable production systems. Requires expertise in machine learning frameworks, data engineering, software architecture, statistical analysis, and model validation. The role may provide technical leadership for distributed teams and requires an active TS/SCI clearance with polygraph.

Develop and integrate cloud software supporting cyber intelligence and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java development, RESTful services, distributed systems, cloud deployment, DevOps, container orchestration, testing, integration, Linux scripting, and troubleshooting. The role also involves big-data technologies, Hadoop ecosystems, LLMs, RAG, analytics, and secure software engineering for national security missions.

Design, develop, test, deploy, and operationalize AI/ML algorithms for mission and business requirements. Build scalable production models, integrate data pipelines, evaluate model performance, and manage training, retraining, monitoring, and continuous improvement. Apply statistical analysis, data governance, software architecture, and data modeling principles. Support system integration and verification/validation activities, with potential leadership of distributed teams and technical customer engagement.

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, deployment, enhancement, maintenance, and issue resolution. The role involves distributed NoSQL systems, big-data technologies, cloud scalability, AI/ML algorithms, LLMs, RAG, agentic workflows, and software testing within a mission-driven national security environment.

Develop machine learning, statistical, data mining, graph-based, and heuristic algorithms for national security applications. Build and optimize AI/ML and large language models, including information retrieval workflows. Analyze varied datasets, create feature vectors, design experiments, evaluate models using standard metrics, produce visualizations, and translate subject matter expertise into production analytics. Guide analytic development efforts and integrate prototype algorithms into analyst workflows.

Designs, develops, tests, deploys, and improves machine learning models for business and mission requirements. Responsibilities include selecting datasets, performing statistical analysis, integrating data pipelines, training and retraining models, monitoring performance, applying evaluation protocols, and overseeing system integration. The role requires knowledge of model architecture, infrastructure, data engineering, governance, and scalable AI/ML solutions, with potential leadership of distributed teams and customer engagement.

Develops machine learning, statistical, data mining, graph-based, and large language models for national security applications. Analyzes datasets, creates feature vectors, prototypes and validates algorithms, builds experiments, generates visualizations, and implements analytics in production frameworks. The role collaborates with subject matter experts, optimizes GPU-based model training, supports LLM and RAG solutions, and guides analytic development efforts. An active TS/SCI clearance with polygraph is required.

Designs, builds, and operationalizes enterprise AI, generative AI, machine learning, and agentic AI solutions. Develops integrations, orchestration services, reusable tooling, deployment pipelines, monitoring, and governance controls. Partners with architecture, platform, data, and product teams to deliver scalable, secure, compliant AI capabilities across the organization.

Leads evaluation, deployment, integration, and operation of secure generative and agentic AI tools, LLMs, and self-hosted multi-GPU infrastructure. Develops AI services, APIs, MCP servers, sandboxed environments, engineering-system integrations, and AI-assisted workflows. Measures reliability and effectiveness, documents recommended practices, supports technical teams, and evaluates emerging technologies across software, robotics, embedded, FPGA, and other engineering domains.

Architects enterprise AI/ML systems, including training pipelines, inference services, feature stores, data ingestion, and MLOps frameworks. Leads multidisciplinary engineering teams, defines technical roadmaps, oversees execution, and interfaces with senior government stakeholders. Establishes secure engineering, DevSecOps, testing, and model governance practices while guiding cloud-native integrations, advanced ML tool adoption, responsible AI, architecture reviews, and strategic planning for Defense Intelligence Agency missions.

Build the enterprise platform for an AI creative product, including workspaces, roles and permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services and APIs for multi-tenant usage patterns alongside polished React/Next.js administration and billing experiences. Collaborate with founders, sales, and customers to convert requirements into production features while taking increasing ownership of enterprise platform surfaces.

Architects, develops, and maintains cloud-native C#/.NET applications and AI-powered solutions on Azure. Designs and deploys AI agents, RAG systems, prompt workflows, tool calling, and vector search capabilities. Establishes engineering standards, makes architectural decisions, conducts code reviews, mentors developers, and translates business needs into technical roadmaps. Integrates responsible AI practices, security, privacy, evaluation, and governance throughout the software development lifecycle.

Lead architecture and implementation of Klaviyo's Customer Agent platform: design scalable agentic/LLM systems, drive technical strategy, ship AI-powered features, ensure reliability and observability, mentor engineers, and partner cross-functionally to deliver conversational AI at scale.

Lead architecture and implementation of Klaviyo's Customer Agent platform. Drive technical strategy for conversational AI at scale, ship AI-powered features, ensure reliability and observability, mentor engineers, and collaborate cross-functionally to evaluate emerging AI technologies and influence platform direction.

Design and build a global, serverless AI inference platform running on heterogeneous GPUs and accelerators. Optimize model scheduling, request routing, caching, and load balancing for low latency. Improve reliability and observability, lead cross-functional projects, and mentor junior engineers.

Plan and execute AI and developer-focused events and content, coordinate cross-functional teams and vendors, manage onsite speaker experience and event operations, maintain event tools and website, track deliverables, mitigate risks, and improve processes to scale community and advocacy programming.

As a Forward Deployed Engineer, you will develop AI agents and automation workflows, collaborate with teams, and ensure secure, reliable solutions integrating with various systems.

Designs and operates secure AI applications and backend microservices on AWS. Responsibilities include threat modeling, secure design reviews, vulnerability remediation, secure API development, AI guardrails, prompt testing, RAG pipelines, monitoring, audit logging, and incident readiness. The role collaborates with engineering, product, cloud infrastructure, and security teams to embed secure-by-design practices throughout the software development lifecycle.

The Founding AI Engineer will develop an end-to-end scan-to-BIM pipeline, focusing on 3D scene understanding and BIM generation from spatial data.

The Lead AI Engineer will build LLM-powered features, optimize AI infrastructure, and lead the execution of AI projects. Responsibilities include designing pipelines, evaluating models, and collaborating with product teams.

The Senior AI Engineer will architect LLM agents, drive ML lifecycle processes, build GenAI solutions, and lead a data engineering team. Responsibilities include overseeing data architecture, scalable deployments, and mentoring.

Lead the architecture and development of a multi-RAT Open RAN digital twin supporting LTE and 5G NR simulation. Integrate production MAC and scheduler software with PHY, channel, UE, traffic, mobility, interference, and network models. Build fidelity tiers, AI/ML evaluation pipelines, reproducible experiments, calibration and validation practices, automated testing, and CI/CD workflows. Improve simulation scalability and reliability while collaborating across wireless, PHY, AI/ML, software, and lab-validation teams.