Top AI Engineer Jobs

Develop AI and machine learning software on an agile Scrum team supporting mission-critical systems. Apply optimization, signal processing, control theory, probabilistic modeling, neural networks, classifiers, and dimensionality reduction. Build and deploy algorithms using high-level programming languages, version control, and potentially containerized, distributed, embedded, GPU-enabled environments. The role is fully onsite in Morrisville, North Carolina, with a 9/80 schedule and approximately 10% travel.

Develop AI and machine-learning software on an agile Scrum team supporting mission-critical systems. Apply optimization, signal processing, control theory, probabilistic modeling, neural networks, classification, and dimensionality-reduction techniques. Program primarily in MATLAB, Python, or R, use Git for version control, and potentially deploy solutions with Docker, Kubernetes, Linux, embedded systems, and GPU programming. The role is fully onsite in Morrisville, North Carolina, follows a 9/80 schedule, requires U.S. citizenship, and requires obtaining a Top Secret/SCI clearance with a counterintelligence polygraph.

Lead backend and AI orchestration systems for CoCounsel Audit: design and operate scalable, low-latency AI workflows, integrate LLMs and retrieval systems, mentor engineers, set platform strategy, and ensure reliability, observability, and auditability at production scale.

Designs and operates secure, scalable cloud infrastructure for AI/ML workloads across hybrid, multi-cloud, multi-enclave, and classified environments. Responsibilities include AI-optimized compute, networking, storage, Kubernetes orchestration, Infrastructure as Code, DevSecOps automation, cloud-native security, data and model governance, high availability, and integration with on-premises systems and GPU hardware. Requires collaboration across teams and active Top Secret clearance.

Build and maintain infrastructure for an automated software agent, including stateful context management, browser automation, traffic-handling systems, prompt optimization, and deterministic logic to reduce token usage. Develop test environments and performance benchmarks, investigate reliability issues, and incorporate user feedback into product improvements. This is a hands-on, fully onsite individual-contributor role requiring close collaboration with leadership and end users.

The job description provides limited role details. The AI Fullstack Engineer will presumably help develop AI-powered software solutions for higher education institutions, supporting administrative automation, student engagement, and data-driven decision-making. Specific responsibilities, technologies, qualifications, experience requirements, compensation, travel, and clearance information are not provided.

Build and operate developer-facing products for embodied AI, including APIs, UIs, packages, documentation, backend services, asset workflows, integrations, and asynchronous processing systems. Convert spatial reconstruction and mapping prototypes into production capabilities, support customer simulator and deployment integrations, diagnose reliability issues, and influence the product roadmap. The role requires full-stack product engineering, customer collaboration, and experience with 3D assets or comparable binary formats.

Drives enterprise AI adoption by understanding business workflows, configuring no-code and low-code agents, apps, and automations, analyzing feedback, developing onboarding materials, and supporting use-case requirements. The role translates business needs into AI solutions, defines KPIs and acceptance criteria, conducts demos, manages support channels, and partners with technical product managers on rollout, adoption, and continuous improvement.

Own reliability, automation, and observability across colocation facilities, on-premises GPU clusters, cloud environments, and customer-facing platforms. Provision and troubleshoot servers, networking, storage, Kubernetes, and hardware; build infrastructure-as-code and automation; manage capacity and cloud spend; create monitoring and alerting; support incident response, on-call operations, and root-cause analysis. Partner with hardware and software teams on CI/CD, QA, HPC workloads, and customer deployments.

Leads the design and hands-on development of production AI solutions, including multi-agent systems, self-improving evaluation loops, open-model integration, multimodal and voice pipelines, and full-stack applications. Engages strategic customers through discovery, workshops, prototypes, and production deployments. Sets technical direction, mentors solutions engineers, develops SambaStack tooling, publishes reference architectures, and communicates product feedback to engineering and product teams.

Develop and deploy production-grade AI and machine learning capabilities for national security and OPIR missions. Responsibilities include signal-processing automation, data processing, model development, evaluation, MLOps workflows, GPU optimization, streaming-data integration, and scalable deployment in operational environments. The role involves collaboration with engineers, mission subject-matter experts, and product teams, with emphasis on AI agents, NLP, LLMs, computer vision, microservices, and classified cloud environments.

Design, develop, and deploy production-grade AI/ML solutions for national security missions, including NLP, LLMs, generative AI, deep learning, and computer vision. Build scalable data pipelines, APIs, microservices, MLOps workflows, and cloud or edge-deployed systems. Optimize and evaluate models, integrate them into secure operational environments, and collaborate with engineering and mission teams. Document technical designs and support accreditation processes in classified settings.

Architect and build a cross-platform design product spanning mobile, desktop, web, cloud services, connected cutting machines, and hardware integrations. Lead AI-native engineering practices, LLM feature integration, architecture governance, secure and reliable connectivity, backend APIs, large-scale modernization, and safe releases. Partner across product, design, QA, firmware, manufacturing, and security while mentoring engineers and remaining approximately 90% hands-on.

Leads architecture and hands-on development of scalable data pipelines, governed data models, semantic layers, analytics products, and AI workflows. Writes production SQL and Python, establishes engineering standards, mentors engineers, and drives design reviews. Builds agentic and LLM solutions using retrieval and managed AI services while ensuring data quality, observability, reliability, privacy, and regulatory compliance for PHI and genomic data. Partners with stakeholders, platform teams, and governance forums to deliver trustworthy business solutions.

Supports scalable ETL/ELT pipelines, cloud data platforms, database integration, machine learning model development and deployment, AI-enabled workflows, and analytics dashboards. Uses Python, data science libraries, AWS, GCP, Azure, SQL and NoSQL databases, Looker, Tableau, and automation tools. Collaborates with data engineers, analysts, data scientists, and marketing teams to deliver data-driven solutions. The role is hybrid in Dallas, requiring two in-office days weekly.

Develop and deploy models to understand technical drawings and specifications, perform multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to deliver customer-impacting applied research.

Develop AI-driven migration capabilities for SnowConvert, including code conversion, data migration, validation pipelines, and automated testing. Build reliable foundations and APIs for agentic AI systems, collaborate with AI and field engineering teams, and optimize distributed data pipelines for performance, scalability, and cost efficiency.

Build AI applications, agents, integrations, and automated workflows that transform technical information into customer documentation. Partner with the Documentation team to use LLMs, agentic AI, and automation to improve content creation, maintenance, publishing, and delivery. Reduce manual effort, increase access to technical knowledge, and identify additional customer-facing workflows where AI can improve efficiency and experience.

Lead the architecture and development of production-grade AI solutions combining LLMs, full-stack engineering, data platforms, cloud architecture, security, and DevOps. Build AI-powered tools for meeting preparation, call summarization, workflow automation, knowledge retrieval, recommendations, and decision support. Partner with engineering, data, product, and business teams to identify opportunities, scale AI applications, and deliver measurable productivity improvements.

Build and deploy production-grade AI agents, workflow automations, and internal applications that improve enterprise processes. Partner with business, operations, engineering, and security teams to identify use cases, design reliable agent architectures, prototype with LLMs and agent frameworks, measure impact, and drive adoption. The role requires strong systems engineering, API, data, and workflow design skills, along with the ability to translate ambiguous business problems into scalable technical solutions.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Designs, tests, governs, and documents AI-assisted workflows for vulnerability and risk analysis in a federal cybersecurity environment. Responsibilities include structured prompt engineering, model parameter testing, hallucination reduction, prompt library management, quality validation, reproducibility benchmarking, and converting CISA cybersecurity workflows into standardized AI templates using enterprise Microsoft Copilot tools.

Develop production software using AI-first engineering practices. Translate requirements into prompts, implementation plans, and acceptance criteria; generate and validate AI-assisted code; identify hallucinations, security issues, and logic gaps; create automated tests; and ensure architecture, performance, documentation, and production readiness. Build reusable prompt workflows, collaborate with product and technical teams, document decisions, and guide engineers in adopting AI-native development practices.

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Designs, deploys, and operationalizes scalable AI microservices, MLOps workflows, production APIs, and ETL/ELT pipelines in secure cloud environments. Responsibilities include model monitoring, drift detection, automated retraining, data orchestration, CI/CD, ATO accreditation, architecture documentation, and stakeholder communication. The role supports Department of War data initiatives that improve situational awareness, operational readiness, and mission execution.